CN110020910A - Object recommendation method and apparatus - Google Patents

Object recommendation method and apparatus Download PDF

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CN110020910A
CN110020910A CN201910063963.9A CN201910063963A CN110020910A CN 110020910 A CN110020910 A CN 110020910A CN 201910063963 A CN201910063963 A CN 201910063963A CN 110020910 A CN110020910 A CN 110020910A
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user
character representation
matrix
indirect
association
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CN110020910B (en
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胡斌斌
张志强
周俊
李小龙
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Advanced New Technologies Co Ltd
Advantageous New Technologies Co Ltd
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Alibaba Group Holding Ltd
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Abstract

Specification discloses a kind of object recommendation method and apparatus.The described method includes: being directed to any user, local feature expression is generated according to the user is expressed as there are the unique characteristics of the affiliated partner of direct correlation relationship with the user；For any object, local feature expression is generated according to the object is expressed as there are the unique characteristics of the association user of direct correlation relationship with the object；Generating the relationship characteristic between the user and the object according to the indirect association relationship between the user and the object indicates；The local feature for calculating the user indicates with relationship characteristic expression and value, and calculates the difference of the local feature expression of described and value and the object；If the difference meets scheduled recommendation condition, the object recommendation is given to the user.

Description

Object recommendation method and apparatus
Technical field
This specification is related to artificial intelligence field more particularly to a kind of object recommendation method and apparatus.
Background technique
With the fast development of Internet technology, the application scenarios for needing to carry out object recommendation are more and more.For example, electric business Platform can be user's Recommendations, and film ticket booking platform can recommend film etc. for user.The accuracy of proposed algorithm will be direct Influence user experience.
Summary of the invention
In view of this, this specification provides a kind of object recommendation method and apparatus.
Specifically, this specification is achieved by the following technical solution:
A kind of object recommendation method, comprising:
For any user, according to there are the unique characteristics of the affiliated partner of direct correlation relationship to be expressed as with the user The user generates local feature and indicates；
For any object, according to there are the unique characteristics of the association user of direct correlation relationship to be expressed as with the object The object generates local feature and indicates；
It is generated between the user and the object according to the indirect association relationship between the user and the object Relationship characteristic indicates；
The local feature for calculating the user indicates with relationship characteristic expression and value, and calculates described and value and institute State the difference that the local feature of object indicates；
If the difference meets scheduled recommendation condition, the object recommendation is given to the user.
A kind of object recommendation device, comprising:
User characteristics generation unit, for any user, according to being associated with pair there are direct correlation relationship with the user The unique characteristics of elephant are expressed as the user and generate local feature expression；
Characteristics of objects generation unit is associated with use there are direct correlation relationship according to the object for any object The unique characteristics at family are expressed as the object and generate local feature expression；
Relationship characteristic generation unit generates the user according to the indirect association relationship between the user and the object Relationship characteristic between the object indicates；
Feature difference computing unit, the local feature for calculating the user indicate with relationship characteristic expression and value, And calculate the difference of described and value with the local feature expression of the object；
Object recommendation unit gives the object recommendation to the user if the difference meets scheduled recommendation condition.
A kind of object recommendation device, comprising:
Processor；
For storing the memory of machine-executable instruction；
Wherein, referred to by reading and executing the machine corresponding with object recommendation logic of the memory storage and can be performed It enables, the processor is prompted to:
For any user, according to there are the unique characteristics of the affiliated partner of direct correlation relationship to be expressed as with the user The user generates local feature and indicates；
For any object, according to there are the unique characteristics of the association user of direct correlation relationship to be expressed as with the object The object generates local feature and indicates；
It is generated between the user and the object according to the indirect association relationship between the user and the object Relationship characteristic indicates；
The local feature for calculating the user indicates with relationship characteristic expression and value, and calculates described and value and institute State the difference that the local feature of object indicates；
If the difference meets scheduled recommendation condition, the object recommendation is given to the user.
User and right is carried by the relationship characteristic expression it can be seen from above description in this specification object recommendation scheme Global information as between can avoid cold start-up problem to a certain extent.Also, above-mentioned object recommendation scheme is to user and right The local message and global information of elephant are merged, and are effectively utilized various auxiliary informations, are greatly improved object recommendation The accuracy of hit rate and recommendation list.
Detailed description of the invention
Fig. 1 is a kind of flow diagram of object recommendation method shown in one exemplary embodiment of this specification.
Fig. 2 is the flow diagram of another object recommendation method shown in one exemplary embodiment of this specification.
Fig. 3 is the process signal for the generation method that a kind of local feature shown in one exemplary embodiment of this specification indicates Figure.
Fig. 4 is that a kind of user shown in one exemplary embodiment of this specification and the relationship characteristic between object indicate to generate The flow diagram of method.
Fig. 5 is a kind of structural schematic diagram for object recommendation device shown in one exemplary embodiment of this specification.
Fig. 6 is a kind of block diagram of object recommendation device shown in one exemplary embodiment of this specification.
Specific embodiment
Example embodiments are described in detail here, and the example is illustrated in the accompanying drawings.Following description is related to When attached drawing, unless otherwise indicated, the same numbers in different drawings indicate the same or similar elements.Following exemplary embodiment Described in embodiment do not represent all embodiments consistent with this specification.On the contrary, they are only and such as institute The example of the consistent device and method of some aspects be described in detail in attached claims, this specification.
It is only to be not intended to be limiting this explanation merely for for the purpose of describing particular embodiments in the term that this specification uses Book.The "an" of used singular, " described " and "the" are also intended to packet in this specification and in the appended claims Most forms are included, unless the context clearly indicates other meaning.It is also understood that term "and/or" used herein is Refer to and includes that one or more associated any or all of project listed may combine.
It will be appreciated that though various information may be described using term first, second, third, etc. in this specification, but These information should not necessarily be limited by these terms.These terms are only used to for same type of information being distinguished from each other out.For example, not taking off In the case where this specification range, the first information can also be referred to as the second information, and similarly, the second information can also be claimed For the first information.Depending on context, word as used in this " if " can be construed to " ... when " or " when ... " or " in response to determination ".
This specification provides a kind of object recommendation scheme.
It on the one hand, can be based on there are the unique characteristics of the affiliated partner of direct correlation relationship to be expressed as user life with user It indicates at local feature, is generated based on the object is expressed as there are the unique characteristics of the association user of direct correlation relationship with object Local feature indicates.
On the other hand, the use for carrying global information can be generated according to the indirect association relationship between user and object Relationship characteristic between family and the object indicates.
Then, can calculate user partial character representation and above-mentioned relation character representation and value, and should and be worth and object The difference that local feature indicates, and the object recommendation can be given to the user when difference meets scheduled recommendation condition.
Relationship characteristic expression in above-mentioned object recommendation scheme carries the global information between user and object, can be one Determine to avoid cold start-up problem in degree.Also, above-mentioned object recommendation scheme is to the local message and global information of user and object Merged, be effectively utilized various auxiliary informations, be greatly improved object recommendation hit rate and recommendation list it is accurate Degree.
Above-mentioned object can include: commodity or service.For example, electric business platform can recommend the commodity sold or service to user.
Above-mentioned object may also include that information.For example, portal website can recommend information to user.
Above-mentioned object may also include that video.For example, video APP (Application, application program) can recommend to user The videos such as TV play, film.
Certainly, in other examples, above-mentioned object can also be other entities or data, and above-mentioned object recommendation can also be applied In other application scenarios, this specification is not particularly limited this.
The realization process of this specification is described below with reference to specific embodiment.
Fig. 1 and Fig. 2 are please referred to, the object recommendation method that this specification provides can comprise the following steps that
Step 102, for any user, according to there are itself spies of the affiliated partner of direct correlation relationship with the user Sign is expressed as the user and generates local feature expression.
Step 104, for any object, according to there are itself spies of the association user of direct correlation relationship with the object Sign is expressed as the object and generates local feature expression.
In the present embodiment, the direct correlation relationship can include: purchase, browsing, comment etc..
By taking object is commodity as an example, there are the commodity of direct correlation relationship with user can include: commodity that user bought, The commodity etc. that commodity that commodity that user browsed, user comment are crossed, user paid close attention to.In general, existing with same user The commodity of direct correlation relationship usually have multiple.
There are the users of direct correlation relationship with commodity can include: bought the user of the commodity, browsed the commodity User, the user for commenting on the commodity, the user for paying close attention to the commodity etc..Similar, exist to be directly linked with same commodity and close The user of system generally also has multiple.
By taking object is film as an example, there are the films of direct correlation relationship with user can include: film that user watched, The film etc. that film that user's evaluation is crossed, user collected.
There are the users of direct correlation relationship with film can include: watched the user of the film, commented on the film User, the user for collecting the film etc..
Referring to FIG. 3, the generating process that the local feature of above-mentioned user and object indicates may include following steps:
Step 302, it is indicated to generate affiliated partner character representation matrix according to the unique characteristics of each affiliated partner.
Step 304, it is indicated to generate association user character representation matrix according to the unique characteristics of each association user.
In the present embodiment, the unique characteristics that can first generate each object indicate, for example, corresponding 0/ can be generated for each object Then 1 vector carries out insertion processing to 0/1 vector, the unique characteristics for obtaining each object indicate.
Above-mentioned 0/1 vector is to indicate object using a very long vector, and the dimension of the vector is the total quantity of object, The element value of dimension is 1 where the object, and the element value of other dimensions is all 0.
It is assumed that one shares 10,000,000 objects, then 0/1 vector of each object is 10,000,000 dimensions, the 1st of object 1 The element value of dimension is 1, other dimension element values are 0, then 0/1 vector of object 1 is represented by [1,0,0,0,0 ...]；Object 2 The element value of the 3rd dimension be 1, other dimension element values are 0, then 0/1 vector of object 2 be represented by [0,0,1,0, 0…]。
In the present embodiment, insertion processing (Embedding) can be carried out to 0/1 vector of each object, by the 0/1 of higher-dimension DUAL PROBLEMS OF VECTOR MAPPING obtains the low-dimensional character representation of each object to lower dimensional space.Since the low-dimensional character representation does not include object The Heterogeneous Informations such as the relationship between attribute, object, therefore the unique characteristics that the low-dimensional character representation can be known as to object indicate.
In the present embodiment, the unique characteristics that can pre-generate all objects indicate, then in the association for determining certain user After object, the unique characteristics for obtaining each affiliated partner are indicated.
Similar, the unique characteristics that can also pre-generate all users indicate, then in the association user for determining certain object Afterwards, the unique characteristics for obtaining each association user indicate.Wherein, the generating process that the unique characteristics of user indicate can refer to object certainly The generating process of body character representation, this is no longer going to repeat them for this specification.
In the present embodiment, the unique characteristics of all affiliated partners of each user indicate to constitute an affiliated partner Character representation matrix.
For example, it is assumed that the unique characteristics expression of affiliated partner is D dimensional vector, certain user has M affiliated partner, then constitutes The affiliated partner character representation matrix of M × D.
The unique characteristics of all association users of each object indicate also to constitute an association user character representation square Battle array.
For example, it is assumed that the unique characteristics of association user indicate to be also D dimensional vector, certain object has N number of association user, then can group At the association user character representation matrix of N × D.
Step 306, institute is generated according to the affiliated partner character representation matrix and the association user character representation matrix State the Harmonious Matrix of affiliated partner and the association user.
In the present embodiment, still with the association user character representation square of the affiliated partner character representation matrix of M × D and N × D For battle array, the product of affiliated partner character representation matrix Yu association user character representation matrix can be calculated, the collaboration of M × N is obtained Matrix.The Harmonious Matrix combines affiliated partner and the unique characteristics of association user and indicates.
It in other examples, can also be first using the neural network model trained to affiliated partner character representation matrix and pass It is combined family character representation matrix and carries out nonlinear transformation, then calculate the product of transformed matrix again, obtain Harmonious Matrix.
Specifically, the first nerves network model that the affiliated partner character representation Input matrix of M × D can have been trained, Such as the matrix of exportable M × D ', for ease of description, the matrix of M × D ' can be known as to convert affiliated partner matrix.Described first Neural network model can be not particularly limited this for models, this specification such as multi-layer perception (MLP)s.
Similar, the nervus opticus network model that the association user character representation Input matrix of N × D can have been trained, example Such as matrix of exportable N × D ', for ease of description, the matrix of N × D ' can be known as to convert association user matrix.
Then, the product that can calculate the transformation affiliated partner matrix of M × D ' and the transformation association user matrix of N × D ', obtains To the Harmonious Matrix of M × N.
Affiliated partner character representation matrix and association user character representation matrix are converted using neural network model, The capability of fitting of model can be enhanced.
Certainly, in other examples, the Harmonious Matrix that other modes generate affiliated partner and association user also can be used, this Specification is not particularly limited this.
Step 308, it carries out the processing of row pondization respectively to the Harmonious Matrix and column pond handles to obtain the affiliated partner With the attention force vector of the association user.
In the present embodiment, the pondization processing can include: maximum pondization processing, average pondization processing, minimum pond Hua Chu Reason etc., this specification is not particularly limited this.
Example is turned to maximum pond, maximum pondization processing on the one hand can be carried out to every row of the Harmonious Matrix of M × N, that is, is directed to Every a line of the Harmonious Matrix chooses greatest member value as the pond of the row as a result, to obtain the attention of affiliated partner Vector, the affiliated partner notice that force vector includes M element.
On the other hand, maximum pondization processing can be carried out to each column of the Harmonious Matrix of M × N, that is, is directed to the Harmonious Matrix Each column, choose greatest member value as the pond of the column as a result, to obtain the attention force vector of association user, association use Family notices that force vector includes N number of element.
In the present embodiment, after carrying out pond processing, softmax function also can be used, normalizing is carried out to attention force vector Change processing, so that each element value of attention force vector is between zero and one, and each element value is 1 with value.
Step 310, the attention force vector based on the affiliated partner character representation matrix and the affiliated partner is described User generates local feature and indicates.
In the present embodiment, the affiliated partner can be paid attention to each element value in force vector as weighted value, difference Be weighted summation to different affiliated partners but the identical element of dimension is come from the affiliated partner character representation matrix, with for The user generates character representation, due to this feature indicate to be based only upon with the user there are the object of direct correlation relationship from Body character representation generates, with reference to there are the information of the user of indirect association relationship and object with the user, therefore can should Character representation is known as local feature expression.
It is assumed that the affiliated partner character representation matrix of M × D is as follows:
Affiliated partner pays attention to force vector are as follows: (w1, w2..., wM), then according to M × D affiliated partner character representation matrix and The affiliated partner pays attention to first element value of the user partial character representation that force vector generates are as follows:
w1×a11+w2×a12+w3×a13+…+wM×a1M,
Second element value are as follows: w1×a21+w2×a22+w3×a23+…+wM×a2M,
Similar, the D element value are as follows:
w1×aD1+w2×aD2+w3×aD3+…+wM×aDM。
Step 312, the attention force vector based on the association user character representation matrix and the association user is described Object generates local feature and indicates.
It is similar with the expression of the local feature of aforementioned user, the association user can be paid attention to each element value in force vector As weighted value, respectively to come from the association user character representation matrix different association users but the identical element of dimension into Row weighted sum is indicated with generating local feature for the object.
In the present embodiment, handle to obtain by the pond to Harmonious Matrix the attention of affiliated partner and association user to Amount is then based on and pays attention to force vector to generate the local feature of user and object and indicate, can be associated with to different affiliated partners, difference User recommends generated influence degree to distinguish this, and then improves the accuracy that subsequent object is recommended.
Step 106, the user and described right is generated according to the indirect association relationship between the user and the object Relationship characteristic as between indicates.
In the present embodiment, the indirect association relationship can refer to be not present between the user and the object and be directly linked Relationship, but can be associated by other users, object.
For example, user Zhang San does not watch flash back past events " hornet ", also there is no close between user Zhang San and " hornet " Other are directly linked relationship for note, comment etc., but the friend Li four of user Zhang San watched " hornet ", in this case user There are indirect association relationships between Zhang San and " hornet ".
For another example user little Bai did not buy iPhone XS, and also there is no close between little Bai and iPhone XS Note, comment, browsing etc. are directly linked relationship, but the friend of little Bai it is small it is black bought iPhone8, little Bai is logical in this case Too small black, iPhone 8, apple brand quotient and iPhone XS are associated, and there is also indirect between little Bai and iPhone XS Incidence relation.
In the present embodiment, referring to FIG. 4, the generating process that the relationship characteristic between user and object indicates may include with Lower step:
Step 402, several indirect reachable paths between the user and the object are obtained, it is described indirectly up to road Diameter passes through one or more nodes and connects the user and the object, and the Lian Bian on the indirect reachable path, which is represented, to be connected Node between have direct correlation relationship, the type of the node include: user class node, object class node and non-user and Non-object class node.
In the present embodiment, the heterogeneous network for carrying various information can first be constructed.
The heterogeneous network may include having user class node, object class node and non-user and non-object class node.For example, It can be believed according to the incidence relation etc. between incidence relation, user and the user between user property, object properties, user and object Breath constructs heterogeneous network, and the non-user and non-object class node in the heterogeneous network can represent user property, object properties etc..
When company's side connecting object of the heterogeneous network and user, which is represented between connected object and user In the presence of the relationship of direct correlation.
When the company side of the heterogeneous network connects user and when user property, which, which represents the user and have, is connected User property.
When the company side of the heterogeneous network connects user and user, which represents has between connected user Friend, the incidence relations such as transfer accounts.
Certainly, the company side in heterogeneous network can also connect user and object properties etc., and this specification is no longer gone to live in the household of one's in-laws on getting married one by one herein It states.
After constructing heterogeneous network, can from the heterogeneous network at random select specified quantity node, then respectively from Selected node sets out, and path extraction is carried out by the way of random walk.
By taking the specified quantity is 1000 as an example, 1000 nodes can be first randomly selected from the heterogeneous network.For The each node chosen, can extract the path of specified hop count, such as 100 from the node by the way of random walk Jump etc..
In the present embodiment, 1000 paths can be extracted from the heterogeneous network, the item number of each path is all 100。
Certainly, in other examples, the item number of each paths can also be not exactly the same, and it is special that this specification does not make this Limitation.
In the present embodiment, pass through the setting to number of nodes and route jumping figure, it is believed that the path extracted carries The most information of heterogeneous network.
In the present embodiment, can judge respectively extract path in whether include between the user and the object between Reachable path is connect, if including, the extraction of indirect reachable path can be carried out.
As an example it is assumed that user node is u2, Object node is m5, and 4 paths are extracted from heterogeneous network, respectively Are as follows:
Path 1:u1-m1-u2-m3-t1-m4-u3-m5-d1-m8；
Path 2:u8-m1-u2-m3-a7-m5；
Path 3:u5-m1-u2-m3-t1-m4-m5；
Path 4:u2-m3-t1-m4.
Wherein, including user u2 to the indirect reachable path between object m5: u2-m3-t1-m4-u3-m5 in path 1；
Also including user u2 to the indirect reachable path between object m5: u2-m3-a7-m5 in path 2；
Also including user u2 to the indirect reachable path between object m5: u2-m3-t1-m4-m5 in path 3；
It does not include user u2 in path 4 to the indirect reachable path between object m5.
In this example, it can extract 3 user u2 to the indirect reachable path between object m5.
Certainly, in other examples, the extraction that other modes carry out indirect reachable path can also be used, this specification is to this It is not particularly limited.
Step 404, it for every indirect reachable path, is indicated according to the unique characteristics of each node on the indirect reachable path Generate the character representation of the indirect reachable path.
By taking aforementioned indirect reachable path u2-m3-t1-m4-u3-m5 as an example, in the present embodiment, it can first obtain this and indirectly may be used Unique characteristics up to node each in path (node u2, m3, t1, m4, u3 and m5) indicate, then integrate the unique characteristics of each node Indicate the character representation of the generation indirect reachable path.
For example, the unique characteristics of each node on the indirect reachable path can be indicated as input, what use had been trained LSTM (Long Short-Term Memory, shot and long term memory network) model exports the character representation of the indirect reachable path.
In the present embodiment, the generation side that the unique characteristics of non-user and non-object class node indicate in indirect reachable path The unique characteristics that method can refer to aforementioned user class node and object class node indicate generation method, and this specification is herein no longer one by one It repeats.
Step 406, the character representation for merging every indirect reachable path, obtains the pass between the user and the object It is character representation.
It can be between user and object after generating character representation for every indirect reachable path based on abovementioned steps 404 The character representations of all indirect reachable paths merged, obtain the relationship characteristic table between the user and the object Show.
In one example, the modes such as summation, averaging can be used to carry out the character representation of the indirect reachable path of each item Fusion.For example, the fortune such as being summed, being averaging to the element value of identical dimensional in the character representation of the indirect reachable path of each item It calculates, obtaining the relationship characteristic indicates.
In another example, the weight of the indirect reachable path can be first determined according to the hop count of every indirect reachable path Then value merges the character representation of the indirect reachable path of each item using weighted sum or average weighted mode.
In general, the hop count of reachable path is fewer indirectly, illustrate that this paths recommends generated influence to get over this Greatly, therefore weight is higher；Conversely, the hop count of reachable path is more indirectly, illustrate that this paths recommends generated shadow to this Sound is smaller, therefore weight is lower.
Indirect reachable path Hop count Weighted value
u2-m3-t1-m4-u3-m5 5 3/12
u2-m3-a7-m5 3 5/12
u2-m3-t1-m4-m5 4 4/12
Table 1
Table 1 is please referred to, still by taking 3 of aforementioned user u2 to object m5 indirect reachable paths as an example, each indirect reachable path Hop count and weighted value can refer to the example of table 1.Wherein, hop count maximum indirect reachable path u2-m3-t1-m4-u3-m5 Weighted value is minimum, and the weighted value of the least indirect reachable path u2-m3-a7-m5 of hop count is maximum.
In this example, each element value in the character representation of the indirect reachable path of each item indirectly can may be used multiplied by place respectively Up to the weighted value in path, then being summed, be averaging etc. for the element value of identical dimensional is calculated, and obtains the user and institute The relationship characteristic stated between object indicates.
Certainly, in other examples, can also be carried out using character representation of the other modes to the indirect reachable path of each item Fusion, this specification are not particularly limited this.
Step 108, the local feature for calculating the user indicates with relationship characteristic expression and value, and described in calculating The difference indicated with the local feature of value and the object.
In the present embodiment, when the local feature indicates and relationship characteristic expression is vector, the office of user can be calculated Then the vector sum of portion's vector and relation vector calculates the vector difference of the vector sum and object partial vector as the difference again It is different.
When the local feature indicates and relationship characteristic expression is matrix, the local matrix and relationship square of user can be calculated The matrix of battle array and, then calculate the matrix again and with the matrix difference of object part matrix as the difference.
Similar, when character representation is other forms, corresponding mode can be used and calculate described and value and difference, this theory This is no longer going to repeat them for bright book.
Step 110, if the difference meets scheduled recommendation condition, the object recommendation is given to the user.
In the present embodiment, the recommendation condition can be determined in the training stage, for example, the recommendation condition can be described Difference is less than discrepancy threshold.
In one example, when the difference is vector, vector field homoemorphism can be calculated, then judges whether vector field homoemorphism is small In discrepancy threshold.
In the present embodiment, when the difference meets the recommendation condition, can illustrate user local feature indicate with The local feature expression of relationship characteristic between user and object indicates and value and object relatively, and then can will be described right As recommending the user.
Relationship characteristic expression carries user in the object recommendation scheme that this specification it can be seen from above description provides Global information between object can avoid cold start-up problem to a certain extent.Also, above-mentioned object recommendation scheme is to user It is merged with the local message and global information of object, is effectively utilized various auxiliary informations, be greatly improved object and push away The accuracy of the hit rate and recommendation list recommended.
In aforementioned embodiment shown in FIG. 1, the first nerves network model, is used the nervus opticus network model It can be determined in training in the parameter and recommendation condition of the models such as the LSTM model for generating indirect reachable path character representation.
It in the present embodiment, can be based on the direct correlation relationship between the user and object occurred in history as positive sample This, is trained above-mentioned model, recommendation condition using method shown in FIG. 1.For example, user little Hua viewing is flashed back past events " in dish Spy 6 ", then user little Hua and film " mission spy 6 " can be trained as a positive sample.
Optionally, to accelerate training, also negative sample can be added during training.For example, user little Hua and film " dish Middle spy 5 " without any direct correlation relationship, then user little Hua and " mission spy 5 " can be trained as a negative sample.
When being trained, loss function can be constructed based on the difference being calculated in abovementioned steps 108, for example, can structure Make hinge loss function loss=max (sJust-sIt is negative+ m, 0), wherein sJustIndicate the difference of positive sample, sIt is negativeIndicate the difference of negative sample Different, m can represent the discrepancy threshold in recommendation condition.
Certainly, in other examples, other modes also can be used to be trained, this specification is not particularly limited this.
Corresponding with the embodiment of aforementioned object recommended method, this specification additionally provides the implementation of object recommendation device Example.
The embodiment of this specification object recommendation device can be using on the server.Installation practice can pass through software It realizes, can also be realized by way of hardware or software and hardware combining.Taking software implementation as an example, as on a logical meaning Device, be to be read computer program instructions corresponding in nonvolatile memory by the processor of server where it Operation is formed in memory.For hardware view, as shown in figure 5, the server where this specification object recommendation device A kind of hardware structure diagram is implemented other than processor shown in fig. 5, memory, network interface and nonvolatile memory Server in example where device can also include other hardware generally according to the actual functional capability of the server, no longer superfluous to this It states.
Fig. 6 is a kind of block diagram of object recommendation device shown in one exemplary embodiment of this specification.
Referring to FIG. 6, the object recommendation device 500 can be applied in aforementioned server shown in fig. 5, include: User characteristics generation unit 501, characteristics of objects generation unit 502, relationship characteristic generation unit 503, feature difference computing unit 504 and object recommendation unit 505.
Wherein, user characteristics generation unit 501, for any user, according to there are direct correlation relationships with the user The unique characteristics of affiliated partner be expressed as the user and generate local feature indicating；
Characteristics of objects generation unit 502, for any object, according to being associated with there are direct correlation relationship with the object The unique characteristics of user are expressed as the object and generate local feature expression；
Relationship characteristic generation unit 503, according to the indirect association relationship generation between the user and the object Relationship characteristic between user and the object indicates；
Feature difference computing unit 504, the local feature for calculating the user indicate the sum indicated with the relationship characteristic Value, and calculating is described and is worth the difference indicated with the local feature of the object；
Object recommendation unit 505 gives the object recommendation to the use if the difference meets scheduled recommendation condition Family.
Optionally, the generating process that the local feature of the user and the object indicates, comprising:
It is indicated to generate affiliated partner character representation matrix according to the unique characteristics of each affiliated partner；
It is indicated to generate association user character representation matrix according to the unique characteristics of each association user；
Described be associated with pair is generated according to the affiliated partner character representation matrix and the association user character representation matrix As the Harmonious Matrix with the association user；
It carries out the processing of row pondization respectively to the Harmonious Matrix and column pond handles to obtain the affiliated partner and the pass It is combined the attention force vector at family；
It is user generation based on the attention force vector of the affiliated partner character representation matrix and the affiliated partner Local feature indicates；
It is object generation based on the attention force vector of the association user character representation matrix and the association user Local feature indicates.
Optionally, described to be generated according to the affiliated partner character representation matrix and the association user character representation matrix The Harmonious Matrix of the affiliated partner and the association user, comprising:
Using the affiliated partner character representation matrix as input, the first nerves network model that use has been trained, which exports, to be become Change affiliated partner matrix；
Using the association user character representation matrix as input, the nervus opticus network model that use has been trained, which exports, to be become Change association user matrix；
The product for calculating the transformation affiliated partner matrix and the transformation association user matrix, obtains the collaboration square Battle array.
Optionally, the attention force vector based on the affiliated partner character representation matrix and the affiliated partner is institute It states user and generates local feature expression, comprising:
The affiliated partner is paid attention into each element value in force vector as weighted value, respectively to the affiliated partner spy Different affiliated partners are come from sign representing matrix but the identical element of dimension is weighted summation, to generate part for the user Character representation；
The attention force vector based on the association user character representation matrix and the association user is the object Generating local feature indicates, comprising:
The association user is paid attention into each element value in force vector as weighted value, respectively to the association user spy Different association users are come from sign representing matrix but the identical element of dimension is weighted summation, to generate part for the object Character representation.
Optionally, the relationship characteristic generation unit 503:
Several indirect reachable paths between the user and the object are obtained, the indirect reachable path passes through one A or multiple nodes connect the user and the object, the Lian Bian on the indirect reachable path represent connected node it Between have direct correlation relationship, the type of the node includes: user class node, object class node and non-user and non-object class Node；
For every indirect reachable path, between indicating that generation is somebody's turn to do according to the unique characteristics of each node on the indirect reachable path Connect the character representation of reachable path；
The character representation for merging every indirect reachable path, obtains the relationship characteristic table between the user and the object Show.
Optionally, several indirect reachable paths obtained between the user and the object, comprising:
Building includes the heterogeneous network of user class node, object class node and non-user and non-object class node, described Lian Bian in heterogeneous network, which is represented, has direct correlation relationship between connected node；
Select the node of specified quantity at random from the heterogeneous network, and respectively from selected node, using with The mode of machine migration carries out path extraction；
From the indirect reachable path obtained in the path extracted between the user and the object.
Optionally, the unique characteristics according to each node on the indirect reachable path indicate to generate the indirect reachable path Character representation, comprising:
The unique characteristics of each node on the indirect reachable path are indicated as input, between being somebody's turn to do using the output of LSTM model Connect the character representation of reachable path.
Optionally, the character representation of every indirect reachable path of the fusion, obtains between the user and the object Relationship characteristic indicate, comprising:
The weighted value of the indirect reachable path is determined according to the hop count of the indirect reachable path；
According to the weighted value, the character representation of every indirect reachable path is merged using average weighted mode, is obtained Relationship characteristic between the user and the object indicates.
Optionally, the scheduled recommendation condition includes: that the difference is less than or equal to discrepancy threshold.
The function of each unit and the realization process of effect are specifically detailed in the above method and correspond to step in above-mentioned apparatus Realization process, details are not described herein.
For device embodiment, since it corresponds essentially to embodiment of the method, so related place is referring to method reality Apply the part explanation of example.The apparatus embodiments described above are merely exemplary, wherein described be used as separation unit The unit of explanation may or may not be physically separated, and component shown as a unit can be or can also be with It is not physical unit, it can it is in one place, or may be distributed over multiple network units.It can be according to actual The purpose for needing to select some or all of the modules therein to realize this specification scheme.Those of ordinary skill in the art are not In the case where making the creative labor, it can understand and implement.
System, device, module or the unit that above-described embodiment illustrates can specifically realize by computer chip or entity, Or it is realized by the product with certain function.A kind of typically to realize that equipment is computer, the concrete form of computer can To be personal computer, laptop computer, cellular phone, camera phone, smart phone, personal digital assistant, media play In device, navigation equipment, E-mail receiver/send equipment, game console, tablet computer, wearable device or these equipment The combination of any several equipment.
Corresponding with the embodiment of aforementioned object recommended method, this specification also provides a kind of object recommendation device, the dress Set includes: processor and the memory for storing machine-executable instruction.Wherein, processor and memory are usually by interior Portion's bus is connected with each other.In other possible implementations, the equipment is also possible that external interface, with can be with other Equipment or component are communicated.
It in the present embodiment, can by reading and executing the machine corresponding with object recommendation logic of the memory storage It executes instruction, the processor is prompted to:
For any user, according to there are the unique characteristics of the affiliated partner of direct correlation relationship to be expressed as with the user The user generates local feature and indicates；
For any object, according to there are the unique characteristics of the association user of direct correlation relationship to be expressed as with the object The object generates local feature and indicates；
It is generated between the user and the object according to the indirect association relationship between the user and the object Relationship characteristic indicates；
The local feature for calculating the user indicates with relationship characteristic expression and value, and calculates described and value and institute State the difference that the local feature of object indicates；
If the difference meets scheduled recommendation condition, the object recommendation is given to the user.
Optionally, in the generation that the local feature of user and the object indicates, the processor is prompted to:
It is indicated to generate affiliated partner character representation matrix according to the unique characteristics of each affiliated partner；
It is indicated to generate association user character representation matrix according to the unique characteristics of each association user；
Described be associated with pair is generated according to the affiliated partner character representation matrix and the association user character representation matrix As the Harmonious Matrix with the association user；
It carries out the processing of row pondization respectively to the Harmonious Matrix and column pond handles to obtain the affiliated partner and the pass It is combined the attention force vector at family；
It is user generation based on the attention force vector of the affiliated partner character representation matrix and the affiliated partner Local feature indicates；
It is object generation based on the attention force vector of the association user character representation matrix and the association user Local feature indicates.
Optionally, institute is being generated according to the affiliated partner character representation matrix and the association user character representation matrix When stating the Harmonious Matrix of affiliated partner and the association user, the processor is prompted to:
Using the affiliated partner character representation matrix as input, the first nerves network model that use has been trained, which exports, to be become Change affiliated partner matrix；
Using the association user character representation matrix as input, the nervus opticus network model that use has been trained, which exports, to be become Change association user matrix；
The product for calculating the transformation affiliated partner matrix and the transformation association user matrix, obtains the collaboration square Battle array.
It optionally, is described in the attention force vector based on the affiliated partner character representation matrix and the affiliated partner When user generates local feature expression, the processor is prompted to:
The affiliated partner is paid attention into each element value in force vector as weighted value, respectively to the affiliated partner spy Different affiliated partners are come from sign representing matrix but the identical element of dimension is weighted summation, to generate part for the user Character representation；
The attention force vector based on the association user character representation matrix and the association user is the object Generating local feature indicates, comprising:
The association user is paid attention into each element value in force vector as weighted value, respectively to the association user spy Different association users are come from sign representing matrix but the identical element of dimension is weighted summation, to generate part for the object Character representation.
Optionally, the user and described right is being generated according to the indirect association relationship between the user and the object When relationship characteristic as between indicates, the processor is prompted to:
Several indirect reachable paths between the user and the object are obtained, the indirect reachable path passes through one A or multiple nodes connect the user and the object, the Lian Bian on the indirect reachable path represent connected node it Between have direct correlation relationship, the type of the node includes: user class node, object class node and non-user and non-object class Node；
For every indirect reachable path, between indicating that generation is somebody's turn to do according to the unique characteristics of each node on the indirect reachable path Connect the character representation of reachable path；
The character representation for merging every indirect reachable path, obtains the relationship characteristic table between the user and the object Show.
Optionally, when obtaining several indirect reachable paths between the user and the object, the processor It is prompted to:
Building includes the heterogeneous network of user class node, object class node and non-user and non-object class node, described Lian Bian in heterogeneous network, which is represented, has direct correlation relationship between connected node；
Select the node of specified quantity at random from the heterogeneous network, and respectively from selected node, using with The mode of machine migration carries out path extraction；
From the indirect reachable path obtained in the path extracted between the user and the object.
Optionally, it indicates to generate the indirect reachable path in the unique characteristics according to each node on the indirect reachable path When character representation, the processor is prompted to:
The unique characteristics of each node on the indirect reachable path are indicated as input, between being somebody's turn to do using the output of LSTM model Connect the character representation of reachable path.
Optionally, it in the character representation for merging every indirect reachable path, obtains between the user and the object When relationship characteristic indicates, the processor is prompted to:
The weighted value of the indirect reachable path is determined according to the hop count of the indirect reachable path；
According to the weighted value, the character representation of every indirect reachable path is merged using average weighted mode, is obtained Relationship characteristic between the user and the object indicates.
Optionally, the scheduled recommendation condition includes: that the difference is less than or equal to discrepancy threshold.
Corresponding with the embodiment of aforementioned object recommended method, this specification also provides a kind of computer-readable storage medium Matter is stored with computer program on the computer readable storage medium, which performs the steps of when being executed by processor
For any user, according to there are the unique characteristics of the affiliated partner of direct correlation relationship to be expressed as with the user The user generates local feature and indicates；
For any object, according to there are the unique characteristics of the association user of direct correlation relationship to be expressed as with the object The object generates local feature and indicates；
It is generated between the user and the object according to the indirect association relationship between the user and the object Relationship characteristic indicates；
The local feature for calculating the user indicates with relationship characteristic expression and value, and calculates described and value and institute State the difference that the local feature of object indicates；
If the difference meets scheduled recommendation condition, the object recommendation is given to the user.
Optionally, the generating process that the local feature of the user and the object indicates, comprising:
It is indicated to generate affiliated partner character representation matrix according to the unique characteristics of each affiliated partner；
It is indicated to generate association user character representation matrix according to the unique characteristics of each association user；
Described be associated with pair is generated according to the affiliated partner character representation matrix and the association user character representation matrix As the Harmonious Matrix with the association user；
It carries out the processing of row pondization respectively to the Harmonious Matrix and column pond handles to obtain the affiliated partner and the pass It is combined the attention force vector at family；
It is user generation based on the attention force vector of the affiliated partner character representation matrix and the affiliated partner Local feature indicates；
It is object generation based on the attention force vector of the association user character representation matrix and the association user Local feature indicates.
Optionally, described to be generated according to the affiliated partner character representation matrix and the association user character representation matrix The Harmonious Matrix of the affiliated partner and the association user, comprising:
Using the affiliated partner character representation matrix as input, the first nerves network model that use has been trained, which exports, to be become Change affiliated partner matrix；
Using the association user character representation matrix as input, the nervus opticus network model that use has been trained, which exports, to be become Change association user matrix；
The product for calculating the transformation affiliated partner matrix and the transformation association user matrix, obtains the collaboration square Battle array.
Optionally, the attention force vector based on the affiliated partner character representation matrix and the affiliated partner is institute It states user and generates local feature expression, comprising:
The affiliated partner is paid attention into each element value in force vector as weighted value, respectively to the affiliated partner spy Different affiliated partners are come from sign representing matrix but the identical element of dimension is weighted summation, to generate part for the user Character representation；
The attention force vector based on the association user character representation matrix and the association user is the object Generating local feature indicates, comprising:
The association user is paid attention into each element value in force vector as weighted value, respectively to the association user spy Different association users are come from sign representing matrix but the identical element of dimension is weighted summation, to generate part for the object Character representation.
Optionally, the indirect association relationship according between the user and the object generates the user and described Relationship characteristic between object indicates, comprising:
Several indirect reachable paths between the user and the object are obtained, the indirect reachable path passes through one A or multiple nodes connect the user and the object, the Lian Bian on the indirect reachable path represent connected node it Between have direct correlation relationship, the type of the node includes: user class node, object class node and non-user and non-object class Node；
For every indirect reachable path, between indicating that generation is somebody's turn to do according to the unique characteristics of each node on the indirect reachable path Connect the character representation of reachable path；
The character representation for merging every indirect reachable path, obtains the relationship characteristic table between the user and the object Show.
Optionally, several indirect reachable paths obtained between the user and the object, comprising:
Building includes the heterogeneous network of user class node, object class node and non-user and non-object class node, described Lian Bian in heterogeneous network, which is represented, has direct correlation relationship between connected node；
Select the node of specified quantity at random from the heterogeneous network, and respectively from selected node, using with The mode of machine migration carries out path extraction；
From the indirect reachable path obtained in the path extracted between the user and the object.
Optionally, the unique characteristics according to each node on the indirect reachable path indicate to generate the indirect reachable path Character representation, comprising:
The unique characteristics of each node on the indirect reachable path are indicated as input, between being somebody's turn to do using the output of LSTM model Connect the character representation of reachable path.
Optionally, the character representation of every indirect reachable path of the fusion, obtains between the user and the object Relationship characteristic indicate, comprising:
The weighted value of the indirect reachable path is determined according to the hop count of the indirect reachable path；
According to the weighted value, the character representation of every indirect reachable path is merged using average weighted mode, is obtained Relationship characteristic between the user and the object indicates.
Optionally, the scheduled recommendation condition includes: that the difference is less than or equal to discrepancy threshold.
It is above-mentioned that this specification specific embodiment is described.Other embodiments are in the scope of the appended claims It is interior.In some cases, the movement recorded in detail in the claims or step can be come according to the sequence being different from embodiment It executes and desired result still may be implemented.In addition, process depicted in the drawing not necessarily require show it is specific suitable Sequence or consecutive order are just able to achieve desired result.In some embodiments, multitasking and parallel processing be also can With or may be advantageous.
The foregoing is merely the preferred embodiments of this specification, all in this explanation not to limit this specification Within the spirit and principle of book, any modification, equivalent substitution, improvement and etc. done should be included in the model of this specification protection Within enclosing.

Claims (19)

1. a kind of object recommendation method, comprising:
For any user, according to the user there are the unique characteristics of the affiliated partner of direct correlation relationship be expressed as it is described User generates local feature and indicates；
For any object, according to the object there are the unique characteristics of the association user of direct correlation relationship be expressed as it is described Object generates local feature and indicates；
The relationship between the user and the object is generated according to the indirect association relationship between the user and the object Character representation；
The local feature for calculating the user indicates and the relationship characteristic indicates and value, and calculate it is described and be worth with it is described right The difference that the local feature of elephant indicates；
If the difference meets scheduled recommendation condition, the object recommendation is given to the user.
2. according to the method described in claim 1, the generating process that the local feature of the user and the object indicates, packet It includes:
It is indicated to generate affiliated partner character representation matrix according to the unique characteristics of each affiliated partner；
It is indicated to generate association user character representation matrix according to the unique characteristics of each association user；
According to the affiliated partner character representation matrix and the association user character representation matrix generate the affiliated partner and The Harmonious Matrix of the association user；
It carries out the processing of row pondization respectively to the Harmonious Matrix and handles to obtain the affiliated partner with column pond to be associated with use with described The attention force vector at family；
Attention force vector based on the affiliated partner character representation matrix and the affiliated partner is that the user generates part Character representation；
Attention force vector based on the association user character representation matrix and the association user is that the object generates part Character representation.
3. according to the method described in claim 2, described according to the affiliated partner character representation matrix and the association user Character representation matrix generates the Harmonious Matrix of the affiliated partner and the association user, comprising:
Using the affiliated partner character representation matrix as input, the first nerves network model output transform that use has been trained is closed Join object matrix；
Using the association user character representation matrix as input, the nervus opticus network model output transform that use has been trained is closed It is combined family matrix；
The product for calculating the transformation affiliated partner matrix and the transformation association user matrix, obtains the Harmonious Matrix.
4. according to the method described in claim 2, described be based on the affiliated partner character representation matrix and the affiliated partner Attention force vector be the user generate local feature indicate, comprising:
The affiliated partner is paid attention into each element value in force vector as weighted value, respectively to the affiliated partner mark sheet Show in matrix from different affiliated partners but the identical element of dimension is weighted summation, for user generation local feature It indicates；
The attention force vector based on the association user character representation matrix and the association user is object generation Local feature indicates, comprising:
The association user is paid attention into each element value in force vector as weighted value, respectively to the association user mark sheet Show in matrix from different association users but the identical element of dimension is weighted summation, for object generation local feature It indicates.
5. according to the method described in claim 1, described raw according to the indirect association relationship between the user and the object It is indicated at the relationship characteristic between the user and the object, comprising:
Obtain several indirect reachable paths between the user and the object, the indirect reachable path by one or Multiple nodes connect the user and the object, and the Lian Bian on the indirect reachable path is represented to be had between connected node There is direct correlation relationship, the type of the node includes: user class node, object class node and non-user and non-object class section Point；
For every indirect reachable path, indicate that generating this indirectly may be used according to the unique characteristics of each node on the indirect reachable path Up to the character representation in path；
The character representation for merging every indirect reachable path, the relationship characteristic obtained between the user and the object indicate.
6. according to the method described in claim 5, several obtained between the user and the object are indirectly reachable Path, comprising:
Building includes the heterogeneous network of user class node, object class node and non-user and non-object class node, the isomery Lian Bian in network, which is represented, has direct correlation relationship between connected node；
The node of specified quantity is selected at random from the heterogeneous network, and respectively from selected node, using random trip The mode walked carries out path extraction；
From the indirect reachable path obtained in the path extracted between the user and the object.
7. according to the method described in claim 5, the unique characteristics according to each node on the indirect reachable path indicate life At the character representation of the indirect reachable path, comprising:
The unique characteristics of each node on the indirect reachable path are indicated to export this as input using LSTM model and indirectly may be used Up to the character representation in path.
8. according to the method described in claim 5, the character representation of every indirect reachable path of the fusion, obtains the user Relationship characteristic between the object indicates, comprising:
The weighted value of the indirect reachable path is determined according to the hop count of the indirect reachable path；
According to the weighted value, the character representation of every indirect reachable path is merged using average weighted mode, is obtained described Relationship characteristic between user and the object indicates.
9. according to the method described in claim 1,
The scheduled recommendation condition includes: that the difference is less than or equal to discrepancy threshold.
10. a kind of object recommendation device, comprising:
User characteristics generation unit, for any user, according to there are the affiliated partners of direct correlation relationship with the user Unique characteristics are expressed as the user and generate local feature expression；
Characteristics of objects generation unit, for any object, according to there are the association users of direct correlation relationship with the object Unique characteristics are expressed as the object and generate local feature expression；
Relationship characteristic generation unit generates the user and institute according to the indirect association relationship between the user and the object The relationship characteristic stated between object indicates；
Feature difference computing unit, the local feature for calculating the user indicates with relationship characteristic expression and value, and counts Calculate the difference of described and value with the local feature expression of the object；
Object recommendation unit gives the object recommendation to the user if the difference meets scheduled recommendation condition.
11. the generating process that the local feature of device according to claim 10, the user and the object indicates, packet It includes:
It is indicated to generate affiliated partner character representation matrix according to the unique characteristics of each affiliated partner；
It is indicated to generate association user character representation matrix according to the unique characteristics of each association user；
According to the affiliated partner character representation matrix and the association user character representation matrix generate the affiliated partner and The Harmonious Matrix of the association user；
It carries out the processing of row pondization respectively to the Harmonious Matrix and handles to obtain the affiliated partner with column pond to be associated with use with described The attention force vector at family；
Attention force vector based on the affiliated partner character representation matrix and the affiliated partner is that the user generates part Character representation；
Attention force vector based on the association user character representation matrix and the association user is that the object generates part Character representation.
12. device according to claim 11, described to be associated with use with described according to the affiliated partner character representation matrix Family character representation matrix generates the Harmonious Matrix of the affiliated partner and the association user, comprising:
Using the affiliated partner character representation matrix as input, the first nerves network model output transform that use has been trained is closed Join object matrix；
Using the association user character representation matrix as input, the nervus opticus network model output transform that use has been trained is closed It is combined family matrix；
The product for calculating the transformation affiliated partner matrix and the transformation association user matrix, obtains the Harmonious Matrix.
13. device according to claim 11, described to be associated with pair based on the affiliated partner character representation matrix with described The attention force vector of elephant is that the user generates local feature expression, comprising:
The affiliated partner is paid attention into each element value in force vector as weighted value, respectively to the affiliated partner mark sheet Show in matrix from different affiliated partners but the identical element of dimension is weighted summation, for user generation local feature It indicates；
The attention force vector based on the association user character representation matrix and the association user is object generation Local feature indicates, comprising:
The association user is paid attention into each element value in force vector as weighted value, respectively to the association user mark sheet Show in matrix from different association users but the identical element of dimension is weighted summation, for object generation local feature It indicates.
14. device according to claim 10, the relationship characteristic generation unit:
Obtain several indirect reachable paths between the user and the object, the indirect reachable path by one or Multiple nodes connect the user and the object, and the Lian Bian on the indirect reachable path is represented to be had between connected node There is direct correlation relationship, the type of the node includes: user class node, object class node and non-user and non-object class section Point；
For every indirect reachable path, indicate that generating this indirectly may be used according to the unique characteristics of each node on the indirect reachable path Up to the character representation in path；
The character representation for merging every indirect reachable path, the relationship characteristic obtained between the user and the object indicate.
15. device according to claim 14, several obtained between the user and the object indirectly may be used Up to path, comprising:
Building includes the heterogeneous network of user class node, object class node and non-user and non-object class node, the isomery Lian Bian in network, which is represented, has direct correlation relationship between connected node；
The node of specified quantity is selected at random from the heterogeneous network, and respectively from selected node, using random trip The mode walked carries out path extraction；
From the indirect reachable path obtained in the path extracted between the user and the object.
16. device according to claim 14, the unique characteristics according to each node on the indirect reachable path are indicated Generate the character representation of the indirect reachable path, comprising:
The unique characteristics of each node on the indirect reachable path are indicated to export this as input using LSTM model and indirectly may be used Up to the character representation in path.
17. device according to claim 14, the character representation of every indirect reachable path of the fusion, obtain the use Relationship characteristic between family and the object indicates, comprising:
The weighted value of the indirect reachable path is determined according to the hop count of the indirect reachable path；
According to the weighted value, the character representation of every indirect reachable path is merged using average weighted mode, is obtained described Relationship characteristic between user and the object indicates.
18. device according to claim 10,
The scheduled recommendation condition includes: that the difference is less than or equal to discrepancy threshold.
19. a kind of object recommendation device, comprising:
Processor；
For storing the memory of machine-executable instruction；
Wherein, by reading and executing the machine-executable instruction corresponding with object recommendation logic of the memory storage, institute Processor is stated to be prompted to:
For any user, according to the user there are the unique characteristics of the affiliated partner of direct correlation relationship be expressed as it is described User generates local feature and indicates；
For any object, according to the object there are the unique characteristics of the association user of direct correlation relationship be expressed as it is described Object generates local feature and indicates；
The relationship between the user and the object is generated according to the indirect association relationship between the user and the object Character representation；
The local feature for calculating the user indicates and the relationship characteristic indicates and value, and calculate it is described and be worth with it is described right The difference that the local feature of elephant indicates；
If the difference meets scheduled recommendation condition, the object recommendation is given to the user.
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