CN110334221A - A kind of interpretation recommended method in knowledge based map path - Google Patents
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Abstract
The present invention discloses a kind of interpretation recommended method in knowledge based map path, the interactive history that this method passes through acquisition user, using interactive history as the subset of knowledge mapping, user-item data set is obtained from subset, under the premise of obtaining subset, the ternary group polling of corresponding knowledge mapping is carried out to subset, and these triples are extracted, the semanteme of composite entity and relationship in triplet information is generated into path representation, is made inferences according to path to infer user preference；After determining a triple path, under the premise of constrained Path length is 4, the routing head entity is inquired to tail entity others path, is indicated with multiple triples；After finding mulitpath, pond operation is carried out to each path to distinguish the contribution that prediction is recommended in different paths；Selection contribution point maximum path is recommended by explaining property of user.It is high that this method recommends precision, and solves the problems, such as the opacity of recommendation.
Description
Technical field
The present invention relates to proposed algorithm technical field, the interpretation recommendation side in specifically a kind of knowledge based map path
Method.
Background technique
Nowadays with the improvement of living standards, people can go more to excavate oneself potential interest, and existing skill
Art is also to aid in people and looks for point of interest,, will be auxiliary in recommended technology by some potential activities or commercial product recommending to user
Supplementary information knowledge mapping introduces recommender system and receives more and more attention, and this mode improves the accuracy recommended, still
Accompanying problem is that: for example, why people will buy the commodity of system recommendation or participate in the activity of system recommendation? this gives system
Recommendation brings opacity problem.The key for solving the problems, such as opacity is that the recommendation is allowed to have interpretation, for example, when one
A recommender system to user's Recommendations or activity when, path and the cause for telling user to recommend the commodity push away user
It recommends system and generates more trusts, to improve the efficiency of recommender system.
In view of the above-mentioned problems, researcher is by exploring in knowledge mapping before this in order to make to recommend to have interpretation
Chain type connection, the connection between user and item can be found to be path, provide supplement abundant to interact for user
Information.This connectivity does not only disclose the semanteme of entity and relationship, and helps to understand the interest of user.However, existing
Work sufficiently studying this connectivity to infer the preference of user, especially in the order dependent to inside track
Property and in terms of whole semanteme modeled.For above-mentioned limitation, the present invention proposes a kind of method, suitable in path by utilizing
Sequence dependence, we allow to carry out effective reasoning to path, to infer user-item interaction basic principle.In addition, setting
Having counted a kind of new weighting pond operation makes our model to distinguish advantage of the different paths in terms of connection user and commodity
With certain interpretation.
Summary of the invention
It is an object of the invention to overcome the deficiencies in the prior art, and provide a kind of knowledge graph based on location-based service field
The recommended method of spectrum, this method recommends precision higher, and solves the problems, such as the opacity of recommendation.
Realizing the technical solution of the object of the invention is:
A kind of interpretation recommended method in knowledge based map path, includes the following steps:
1) the Extraction Projects entity from the interactive history of user record, obtains user-item entity data set, solid data
Collect the subset as knowledge mapping KG；
2) entity data set for obtaining step 1), it is corresponding with knowledge mapping KG entity, corresponding ternary group polling is carried out,
Entity-relation table is obtained, going out to be used together triple with Relation extraction for the entity in table indicates；
3) triple obtained according to step 2) generates road with the semanteme of composite entity and relationship in triplet information
Diameter indicates, and infers user preference according to path, determines a favored pathway；
4) favored pathway obtained according to step 3) inquires the routing head entity under the premise of constrained Path length is 4
Path is reached to tail entity others, obtains mulitpath, each path is indicated with multiple triples；
5) mulitpath obtained according to step 4) to each path distinguish different paths to prediction recommend contribution not
Together, it then carries out pond operation and calculates different path contributions, and the maximum paths of contribution point is selected to carry out interpretable push away
It recommends.
The path is that the triple in knowledge mapping KG clearly illustrates the attribute of a relation of direct or indirect item,
The attribute constitutes given one or more path of user and item between, is expressed as follows:
It is the sequence of entity and relationship, sequence table by the path formal definition of user u to project i in knowledge mapping KG
Up to formula are as follows:Wherein e1=u, eL=i；(el,rl,el+1) it is first of triple in p, and
L indicates the number of triple in path.
The contribution that prediction is recommended in the different paths of differentiation in step 5), specific as follows:
User-item entity is to a series of path for usually having paths to connect them in knowledge mapping, if s={ s1,
s2,…,skBe K paths prediction score, P (u, i)={ p1,p2,…,pkIt is path set of the connection user-item to (u, i)
It closes, wherein each element is the average value for being predicted as all path scores calculated according to formula, last, indicates are as follows:
WhereinIndicate that the prediction average mark in all paths, σ (x) indicate activation primitive, wherein x expression parameter, K are road
Diameter item number.
The prediction score is projected to end-state in the prediction score of output using two full communicating layers,
Calculation is as follows:
Wherein W1And W2The respectively coefficient weights in first layer path and second layer path, ReLU (x) are activation primitive,
Middle x is activation parameter, pkIndicate kth paths.
In step 5), the pond operation calculates different path contributions, specific as follows:
A weighting pond operation is designed to polymerize the score in all paths, pond function is defined as follows:
Wherein log [] is mathematical function, and K is path number, and exp () is exponential function, SkScore, γ are predicted for path
It is hyper parameter to control each weighted index；The importance in path is distinguished, the importance in path is determined by gradient, pressure gradient expression formula
It is as follows:
Wherein exp () is exponential function, ∑kIndicate that summation meets, SkIt is path prediction score, γ is hyper parameter to control
Each weighted index, calculated result is directly proportional to the score of each path in backpropagation step, and pond function assigns final prediction
Greater flexibility, in particular, the pond function in maximum pond may degenerate, however, it can be by setting when setting γ → 0
Setting γ → ∞ reduces average pond.
A kind of interpretation recommended method in knowledge based map path provided by the invention, this method is by knowledge mapping road
Diameter is combined with recommendation, solves the problems, such as the opacity of conventional recommendation, is improved and is recommended precision and make to recommend to have interpretable
Property；And using the auxiliary information knowledge mapping of specific network layered structure, so that recommendation results more have a sense of hierarchy, has and push away
It is good to recommend effect, the characteristics of sustainable utilization.
Detailed description of the invention
Fig. 1 is a kind of flow chart of the interpretation recommended method in knowledge based map of the invention path；
Fig. 2 is case result figure.
Specific embodiment
The content of present invention is further elaborated with reference to the accompanying drawings and examples, but is not limitation of the invention.
Embodiment:
A kind of interpretation recommended method in knowledge based map path, process is as shown in Figure 1, below with reference to cinematic data
For specifically introduced, cinematic data is as shown in table 1 below, in conjunction with introduced for film user u4825 method include it is as follows
Step:
1) it watches from user or with Extraction Projects entity movie name in the interactive history record for clicking film, is used
Family-item entity data set, subset of the entity set as film knowledge mapping KG；
2) entity data set obtained according to step 1), it is corresponding with knowledge mapping KG entity, it carries out corresponding triple and looks into
It askes, obtains entity-relation table, going out to be used together triple with Relation extraction for the entity in table indicates；
3) triple (user, relationship, film) information obtained according to step 2), with the composite entity in triplet information
Path representation is generated with the semanteme of relationship, and infers film user preference according to path, determines a favored pathway；
4) favored pathway obtained according to step 3) inquires the routing head entity under the premise of constrained Path length is 4
Path is reached to tail entity others, obtains mulitpath, each path is indicated with multiple triples；
5) mulitpath obtained according to step 4) to each path distinguish different paths to prediction recommend contribution not
Together, it then carries out pond operation and calculates different path contributions, and the maximum paths of contribution point is selected to carry out interpretable push away
It recommends, result is as shown in Fig. 2, we can choose the maximum paths progress interpretation of a contribution point as can be seen from the results
Recommendation can preferably excavate the interest of user.
6) according to above-mentioned steps, We conducted experimental analyses, for two item number of AUC (clicking rate) and ACC (accuracy rate)
According to being analyzed, the two has respectively reached 0.924 and 0.849, this embodies the feasibility and accuracy of our methods.
The path is that the triple in knowledge mapping KG clearly illustrates direct or indirect (i.e. multi-step) item
Attribute of a relation, the attribute constitute given one or more path of user and item between, are expressed as follows:
It is the sequence of entity and relationship by the path formal definition of user u to project i in knowledge mapping KG:Wherein e1=u, eL=i；(el,rl,el+1) it is first of triple in p, and L indicates road
The number of triple in diameter.
The contribution that prediction is recommended in the different paths of differentiation in step 5), specific as follows:
User-item entity is to a series of path for usually having paths to connect them in knowledge mapping, if s={ s1,
s2,…,skBe K paths prediction score, P (u, i)={ p1,p2,…,pkIt is path set of the connection user-item to (u, i)
It closes, wherein each element is the average value for being predicted as all path scores calculated according to formula, last, indicates are as follows:
WhereinIndicate that the prediction average mark in all paths, σ (x) indicate activation primitive, wherein x expression parameter, K are path
Item number.
The prediction score is projected to end-state in the prediction score of output using two full communicating layers,
Calculation is as follows:
Wherein W1And W2The respectively coefficient weights in first layer path and second layer path, ReLU (x) are activation primitive,
Middle x is activation parameter, pkIndicate kth paths.
In step 5), the pond operation calculates different path contributions, specific as follows:
A weighting pond operation is designed to polymerize the score in all paths, pond function is defined as follows:
Wherein log [] is mathematical function, and K is path number, and exp () is exponential function, SkScore, γ are predicted for path
It is hyper parameter to control each weighted index；The importance in path is distinguished, the importance in path is determined by gradient, pressure gradient expression formula
It is as follows:
Wherein exp () is exponential function, ∑kIndicate that summation meets, SkIt is path prediction score, γ is hyper parameter to control
Each weighted index, calculated result is directly proportional to the score of each path in backpropagation step, and pond function assigns final prediction
Greater flexibility, in particular, the pond function in maximum pond may degenerate, however, it can be by setting when setting γ → 0
Setting γ → ∞ reduces average pond.
1 book data collection of table
Claims (5)
1. a kind of interpretation recommended method in knowledge based map path, which comprises the steps of:
1) the Extraction Projects entity from the interactive history of user record, obtains user-item entity data set, and entity data set is made
For the subset of knowledge mapping KG；
2) entity data set for obtaining step 1), it is corresponding with knowledge mapping KG entity, corresponding ternary group polling is carried out, is obtained
Entity-relation table, going out to be used together triple with Relation extraction for the entity in table indicates；
3) triple obtained according to step 2) generates routing table with the semanteme of composite entity and relationship in triplet information
Show, and user preference is inferred according to path, determines a favored pathway；
4) favored pathway obtained according to step 3) inquires the routing head entity to tail under the premise of constrained Path length is 4
Entity others reach path, obtain mulitpath, each path is indicated with multiple triples；
5) mulitpath obtained according to step 4) distinguishes the difference for the contribution that prediction is recommended in different paths to each path, into
The operation of row pond calculates different path contributions, and the maximum paths of contribution point is selected to carry out interpretable recommendation.
2. a kind of interpretation recommended method in knowledge based map path according to claim 1, which is characterized in that institute
The path stated is that the triple in knowledge mapping KG clearly illustrates the attribute of a relation of direct or indirect item, which is constituted
Given one or more path of user and item between, is expressed as follows:
It is the sequence of entity and relationship, sequence expression formula by the path formal definition of user u to project i in knowledge mapping KG
Are as follows:Wherein e1=u, eL=i；(el,rl,el+1) it is first of triple in p, and L table
Show the number of triple in path.
3. a kind of interpretation recommended method in knowledge based map path according to claim 1, which is characterized in that institute
The contribution that prediction is recommended in the different paths of differentiation in step 5) is stated, specific as follows:
User-item entity is to a series of path for having paths to connect them in knowledge mapping, if s={ s1,s2,…,skIt is K
The prediction score of paths, P (u, i)={ p1,p2,…,pkIt is set of paths of the connection user-item to (u, i), wherein each
Element is the average value for being predicted as all path scores calculated according to formula, last, is indicated are as follows:
WhereinIndicate that the prediction average mark in all paths, σ (x) indicate activation primitive, wherein x expression parameter, K is path item
Number.
4. a kind of interpretation recommended method in knowledge based map path according to claim 3, which is characterized in that institute
The prediction score stated is projected to end-state in the prediction score of output using two full communicating layers, and calculation is such as
Under:
Wherein W1And W2The respectively coefficient weights in first layer path and second layer path, ReLU (x) are activation primitive, and wherein x is
Activation parameter, pkIndicate kth paths.
5. a kind of interpretation recommended method in knowledge based map path according to claim 1, which is characterized in that step
It is rapid 5) in, the described pond operation calculates different path contributions, specific as follows:
A weighting pond operation is designed to polymerize the score in all paths, pond function is defined as follows:
Wherein log [] is mathematical function, and K is path number, and exp () is exponential function, SkScore is predicted for path, and γ is super ginseng
Number is to control each weighted index；The importance in path is distinguished, the importance in path is determined that pressure gradient expression formula is as follows by gradient:
Wherein exp () is exponential function, ∑kIndicate that summation meets, SkIt is path prediction score, γ is that hyper parameter is each to control
Weighted index, calculated result is directly proportional to the score of each path in backpropagation step, and it is bigger that pond function assigns final prediction
Flexibility, when setting γ → 0, the pond function in maximum pond may degenerate, and pass through setting γ → ∞ reduction and be averaged pond.
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| CN113656709B (en) * | 2021-08-24 | 2023-07-25 | 东北大学 | Interpretable interest point recommendation method integrating knowledge graph and time sequence characteristics |
| CN113656709A (en) * | 2021-08-24 | 2021-11-16 | 东北大学 | Interpretable interest point recommendation method fusing knowledge graph and time sequence characteristics |
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