CN106354886B - The method of potential neighbor relational graph screening nearest-neighbors is utilized in recommender system - Google Patents

The method of potential neighbor relational graph screening nearest-neighbors is utilized in recommender system Download PDF

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CN106354886B
CN106354886B CN201610909600.9A CN201610909600A CN106354886B CN 106354886 B CN106354886 B CN 106354886B CN 201610909600 A CN201610909600 A CN 201610909600A CN 106354886 B CN106354886 B CN 106354886B
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王晓军
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Nanjing Post and Telecommunication University
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Abstract

The invention discloses the methods that potential neighbor relational graph screening nearest-neighbors are utilized in recommender system, comprising the following steps: (1) generating, there is the object gathering of redundancy properties to close C；(2) building gathering closes the corresponding potential neighbor relational graph of C；(3) quantify each edge weight in potential neighbor relational graph, while weight indicate while two objects being abutted become nearest-neighbors a possibility that；(4) potential neighbor relationship is cut, rejects extra comparison；(5) nearest-neighbors of the potential neighbor relational graph screening target after cutting are utilized.Recommendation based on complete large-scale dataset is mapped to the recommendation of a lesser data set of scale under the premise of ensureing the precision recommended by this screening technique, is reduced recommender system scale, is guaranteed the high efficiency of recommended method.

Description

The method of potential neighbor relational graph screening nearest-neighbors is utilized in recommender system
Technical field
The present invention relates to recommended technology fields, screen arest neighbors using potential neighbor relational graph especially in recommender system The method in residence.
Background technique
Current ever-increasing data volume makes user need to expend a great deal of time just find valuable information.Collaboration Filtering is considered as one of the effective technology for solving information overload problem, is widely used to film, music, books, travelling, new News etc. recommends field.The main thinking of collaborative filtering method is to the hobby prediction target user of project according to similar users to item Purpose viewpoint, or implement to recommend for destination item according to opinion of the user to similar terms.Therefore, collaborative filtering needs to solve A critical issue be: how the nearest-neighbors of effective selection target user or project.But recommender system possesses millions of User and project, scale still sustainable growth are counted, nearest-neighbors is searched in mass data with conventional method and is difficult to ensure and closing It manages and accurate recommendation is provided in the time.
The core of collaborative filtering method is to need to compare all items in recommender system (or user) two-by-two, is passed through Similarity between calculating selects nearest-neighbors.The number compared two-by-two is more, and the efficiency of operation is lower, but it is searched for A possibility that nearest-neighbors, is higher；Otherwise the number compared two-by-two is fewer, and operational efficiency is higher, but misses nearest-neighbors Possibility is also higher, to influence recommendation precision.Data internal association is in close relations and complicated in recommender system in addition, valence It is extremely unbalanced to be worth Density Distribution, the calculating of mass data in recommender system cannot be depended on as Small Sample Database collection to complete The statistical analysis of office data and iterative calculation need the on-demand reduction method of heuristic data.
Summary of the invention
It is provided the technical problem to be solved by the present invention is to overcome the deficiencies in the prior art in recommender system using latent In the method for neighborhood figure screening nearest-neighbors, the method for the present invention both ensures recommendation effect to reject redundancy and extra comparison Rate, and recommendation precision is not sacrificed, it is ensured that accurate recommendation is provided within reasonable time.
The present invention uses following technical scheme to solve above-mentioned technical problem:
The method for screening nearest-neighbors using potential neighbor relational graph in recommender system proposed according to the present invention, including Following steps:
Step 1, to set i ∈ O, O be the object set for needing to screen nearest-neighbors, and i is object, using Fuzzy clustering techniques foundation Object i is assigned in multiple clusters by preset probability, thus generates the cluster containing K object cluster by the feature vector of object Set C；
Step 2, building gathering close the corresponding potential neighbor relational graph G of CC={ VC,EC, wherein VCIt is vertex set, ECIt is Undirected line set；It is specific as follows:
If object i and object j appears in gathering simultaneously and closes in the same cluster c of C, object i and j be referred to as co-occurrence to and remember For < i, j >；For each pair of co-occurrence in gathering conjunction C to < i, j >, first by the corresponding vertex v of object i and jiAnd vjIt is added to figure GCIn, if not there is nonoriented edge between two objects i and j, use side ei,jConnect vertex viAnd vj；Wherein, scheme GCIn every Side ei,jIndicate a potential neighborhood, side ei,jTwo adjacent vertex vsiAnd vjCorresponding object i and j is known as contiguous object, J ∈ O, c ∈ C, vi∈VC, vj∈VC, ei,j∈EC；
Step 3, quantization figure GCThe weight of middle each edge；
Step 4, to figure GCIt is cut, deletes potential neighbor relational graph GCThe weight on middle side is lower than wminSide, remaining side Constitute a new figure GC'；Wherein, wminFor the minimal weight threshold value of setting；
Step 5 chooses object i as target, utilizes the potential neighbor relational graph G after cuttingC' screening target arest neighbors It occupies, for GC' in figure target i every adjacent side ei,j, relatively and calculate utility vector RiWith RjBetween similarity, then according to Its nearest-neighbors is screened in all of its neighbor object of target i according to neighbour's alternative condition；Wherein, RiIndicate object i effectiveness to Amount, RjIndicate the utility vector of object j.
As it is of the present invention in recommender system using the method for potential neighbor relational graph screening nearest-neighbors into one Prioritization scheme is walked, uses following formula to calculate side e in the step 3i,jWeight ei,j.weight:
Wherein, CiIndicate that the gathering that object i is subordinate to is closed, CjIndicate that the gathering that object j is subordinate to is closed, Ci,jIt is shared for object i and j Set,| * | for membership in set *, d (*) indicates the degree of vertex *.
As it is of the present invention in recommender system using the method for potential neighbor relational graph screening nearest-neighbors into one Prioritization scheme is walked, neighbour's alternative condition refers to that the maximum preceding k object of the similarity of selection and target constitutes mesh in the step 5 Target neighbour collection.
As it is of the present invention in recommender system using the method for potential neighbor relational graph screening nearest-neighbors into one Walk prioritization scheme, K >=1.
As it is of the present invention in recommender system using the method for potential neighbor relational graph screening nearest-neighbors into one Walk prioritization scheme, side ei,jWeight and relational graph GCMiddle side ei,jThe object i and j abutted shared cluster is related.
The invention adopts the above technical scheme compared with prior art, has following technical effect that
Under the premise of ensureing recommendation precision, it is lesser that the recommendation based on complete large-scale dataset is mapped to a scale The recommendation of data set, and recommender system scale is significantly reduced, guarantee the high efficiency of recommended method.Specific manifestation is in the following areas:
(1) object is assigned in multiple clusters by certain probability using Fuzzy clustering techniques, this redundancy properties provide A kind of indispensable, reliable, method for avoiding missing nearest-neighbors；
(2) in cluster process, by the high object assignment of similitude into the same cluster, the object that peels off individually is excluded.This It is not only advantageous for improving and recommends precision, while decreasing the extra comparison of object bring that peels off, improve and recommend efficiency；
(3) in the building process of potential neighbor relational graph, each pair of co-occurrence is to a line at most corresponding diagram, each edge table Show that a kind of potential neighborhood, subsequent step are only compared the contiguous object of each edge, has calculated the similar of them Degree；Therefore, redundancy and extra co-occurrence pair are eliminated, the number compared two-by-two in object set is reduced, improves the effect of recommendation Rate；
(4) by the weight threshold on the appropriate side of setting, the side for being lower than weight in potential neighbor weighted connections figure is rejected, into One step eliminates extra comparison.
Detailed description of the invention
Fig. 1 is process flow of the invention.
Specific embodiment
Technical solution of the present invention is described in further detail with reference to the accompanying drawing:
If Fig. 1 is process flow of the invention, following steps: step (1) generates the object gathering conjunction with redundancy properties C；Step (2), building gathering close the corresponding potential neighbor relational graph of C；Each edge in step (3), quantization potential neighbor relational graph Weight, while weight indicate while two objects being abutted become nearest-neighbors a possibility that；Step (4) closes potential neighbor System is cut, and extra comparison is rejected；Step (5) utilizes the arest neighbors of the potential neighbor relational graph screening target after cutting It occupies.
[embodiment 1]
In a film recommender system, it is known that user collects U, and Item Sets I, each project is a film；FiIt indicates The feature vector of project i, for describing film school, prize-winning type, performer and director etc.；RiThe effectiveness of expression project i ∈ I to Amount, ru,i∈RiIt is scoring of the user u ∈ U to project i.Recommender system screens destination item i most using potential neighbor relational graph Neighbour occupies, and then utilizes scoring of the target user u to the score in predicting user u of all nearest-neighbors of project i to project i.At this In embodiment 1, object set is project set, and detailed process is as follows for the nearest-neighbors of screening programme:
(1) gathering is generated to close.Project i ∈ I is assigned to by certain probability according to item characteristic using Fuzzy clustering techniques In multiple clusters, thus generates project gathering and close C.
(2) building gathering closes the corresponding potential neighbor relational graph G of CC={ VC,EC, VCIt is vertex set, ECIt is nonoriented edge collection It closes.Scheme GCSpecific construction method is as follows: for co-occurrence each pair of in C to < i, j >, first by the corresponding vertex project i ∈ I and j ∈ I viAnd vjIt is added to figure GCIn, then use side ei,j∈ECConnect vertex viAnd vj.During creating relational graph, if discovery two A nonoriented edge is had existed between a project, then no longer needs to increase a line between them.Each edge indicates that one is latent in figure Neighborhood, similarity calculation need to be passed through in the next steps, it is determined whether be nearest-neighbors.
(3) quantization figure GCEach edge weight.Relational graph GCAfter finishing, for further screening neighbor relationships, requirement figure Middle each edge.Weighing computation method is as follows: knownIt respectively indicates the gathering that project i, j is subordinate to close, Ci,j=Ci ∩CjReferred to as project i and j shared set.When project i and j share cluster it is more (i.e. | Ci,j| value is bigger), they become each other A possibility that nearest-neighbors, is bigger.In addition to considering | Ci,j|, side right escheat need to consider the sum of its adjoining project cluster subjected, And the degree of the adjacent vertex on side.When the cluster that the adjoining project on some side is subordinate to is fewer, the weight Ying Yuegao on the side；When some side Vertex degree it is smaller, the weight Ying Yuegao on the side.Side e is calculated with following formulai,j∈ECWeight
Wherein, d (*) indicates the degree of vertex *.
(4) extra comparison is cut.Minimal weight threshold value w according to settingminDelete figure GCThe weight on middle side is lower than wmin's Side, remaining side constitute the figure G after cuttingC'。
(5) project i is chosen as target, screens the nearest-neighbors of target i.For GC'Every adjacent side of target i in figure ei,j, compare the utility vector R of target i and j project i in user-project utility matrixiWith the utility vector R of project jjBetween Similitude, calculate the similarity between them；Then it is screened in all of its neighbor project of target i according to neighbour's alternative condition Its nearest-neighbors is chosen the neighbour for constituting target with the maximum preceding k project of the similarity of destination item and is collected in this example 1. Scoring of the score in predicting target user u of each neighbour to destination item finally is concentrated to project neighbour using target user u.
[embodiment 2]
In a film recommender system, it is known that user collects U, and Item Sets I, each project is a film；FqIt indicates The feature vector of user q, gender, age, occupation etc. for describing user；RqIndicate the utility vector of user q ∈ U, rq,m∈ RuIt is scoring of the user q to project m ∈ I.Recommender system screens the nearest-neighbors of target user q using potential neighbor relational graph, Then the scoring using all nearest-neighbors of target user q to the score in predicting user q of project m to project m.In the present embodiment In 2, object set is user's set, and screening the nearest-neighbors of user, detailed process is as follows:
(1) gathering is generated to close.User u ∈ U is assigned to by certain probability according to user characteristics using Fuzzy clustering techniques In multiple clusters, thus generates user's gathering and close C.
(2) building gathering closes the corresponding potential neighbor relational graph G of CC={ VC,EC, VCIt is vertex set, ECIt is nonoriented edge collection It closes.Scheme GCSpecific construction method is as follows: for co-occurrence each pair of in C to < q, h >, first by the corresponding vertex user q ∈ U and h ∈ U vqWithvhIt is added to figure GCIn, then use side eq,h∈ECConnect vertex vqWithvh.During creating relational graph, if discovery two A nonoriented edge is had existed between user, then no longer needs to increase a line between them.Each edge indicates that one is potential in figure Neighborhood, similarity calculation need to be passed through in the next steps, it is determined whether be nearest-neighbors.
(3) quantization figure GCEach edge weight.Relational graph GCAfter finishing, for further screening neighbor relationships, requirement figure Middle each edge.Weighing computation method is as follows: knownIt respectively indicates the gathering that user q, h are subordinate to close, Cq,h=Cq ∩ChReferred to as user q and h shared set.When user q and h share cluster it is more (i.e. | Cq,h| value is bigger), they become each other A possibility that nearest-neighbors, is bigger.In addition to considering | Cq,h|, side right escheat need to consider that it abuts the sum of user's cluster subjected, And the degree of the adjacent vertex on side.When the cluster that the adjoining user on some side is subordinate to is fewer, the weight Ying Yuegao on the side；When some side Vertex degree it is smaller, the weight Ying Yuegao on the side.Therefore, side e can be calculated with following formulaq,hWeight:
Wherein, d (*) indicates the degree of vertex *.
(4) extra comparison is cut.Minimal weight threshold value w according to settingminDelete figure GCThe weight on middle side is lower than wmin's Side, remaining side constitute the figure G after cuttingC'。
(5) user q is chosen as target, screens the nearest-neighbors of target user q.For GC' in figure target q every neighbour Edge fit eq,h, compare the utility vector R of user q in user-project utility matrix of target q and hqWith the utility vector of user h RhBetween similitude, calculate the similarity between them；Then all of its neighbor user of the foundation neighbour's alternative condition in target q Middle its nearest-neighbors of screening are chosen in this example 2 and constitute target with the maximum preceding k user of the similarity of target user q Neighbour's collection.Finally concentrate each neighbour to the score in predicting target user q of project to destination item using the neighbour of target user q Scoring.
Specific embodiments described above has carried out further the purpose of the present invention, technical scheme and beneficial effects Detailed description, it should be understood that being not limited to this hair the foregoing is merely specific embodiments of the present invention Bright range, any those skilled in the art, that is made under the premise of not departing from design and the principle of the present invention is equal Variation and modification, should belong to the scope of protection of the invention.

Claims (4)

1. utilizing the method for potential neighbor relational graph screening nearest-neighbors in recommender system, which is characterized in that including following step It is rapid:
Step 1, to set i ∈ O, O be the object set for needing to screen nearest-neighbors, and i is object, using Fuzzy clustering techniques according to object Feature vector, object i is assigned in multiple clusters by preset probability, the gathering containing K object cluster is thus generated and closes C；
Step 2, building gathering close the corresponding potential neighbor relational graph G of CC={ VC,EC, wherein VCIt is vertex set, ECIt is undirected Line set；It is specific as follows:
If object i and object j appears in gathering simultaneously and closes in the same cluster c of C, object i and j be referred to as co-occurrence to and be denoted as < I, j >；For each pair of co-occurrence in gathering conjunction C to < i, j >, first by the corresponding vertex v of object i and jiAnd vjIt is added to figure GCIn, If not there is nonoriented edge between two objects i and j, side e is usedi,jConnect vertex viAnd vj；Wherein, scheme GCMiddle each edge ei,j Indicate a potential neighborhood, side ei,jTwo adjacent vertex vsiAnd vjCorresponding object i and j is known as contiguous object, j ∈ O, C ∈ C, vi∈VC, vj∈VC, ei,j∈EC；
Step 3, quantization figure GCThe weight of middle each edge；
Side e is calculated using following formula in step 3i,jWeight ei,j.weight:
Wherein, CiIndicate that the gathering that object i is subordinate to is closed, CjIndicate that the gathering that object j is subordinate to is closed, Ci,jThe collection shared for object i and j It closes,Ci,j=Ci∩Cj, | * | for membership in set *, d (*) indicates the degree of vertex *；
Step 4, to figure GCIt is cut, deletes potential neighbor relational graph GCThe weight on middle side is lower than wminSide, remaining side is constituted One new figure GC'；Wherein, wminFor the minimal weight threshold value of setting；
Step 5 chooses object i as target, utilizes the potential neighbor relational graph G after cuttingC'Screen the nearest-neighbors of target, needle To GC'Every adjacent side e of target i in figurei,j, relatively and calculate utility vector RiWith RjBetween similarity, then according to close Adjacent alternative condition screens its nearest-neighbors in all of its neighbor object of target i；Wherein, RiIndicate the utility vector of object i, Rj Indicate the utility vector of object j.
2. the method according to claim 1 for screening nearest-neighbors using potential neighbor relational graph in recommender system, It is characterized in that, neighbour's alternative condition refers to that the maximum preceding k object of the similarity of selection and target constitutes target in the step 5 Neighbour collection.
3. the method according to claim 1 for screening nearest-neighbors using potential neighbor relational graph in recommender system, It is characterized in that, K >=1.
4. the method according to claim 1 for screening nearest-neighbors using potential neighbor relational graph in recommender system, It is characterized in that, side ei,jWeight and relational graph GCMiddle side ei,jThe object i and j abutted shared cluster is related.
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