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Product recommendation method and system combining pairwise optimization and matrix factorization

A matrix decomposition and product technology, applied in the field of product recommendation method and system combining pairwise optimization and matrix decomposition, can solve the problems of high time complexity, high dependence of matrix decomposition model on data quality, neglect of user-to-user correlation, etc. problem, to achieve the effect of improving the accuracy

Active Publication Date: 2021-05-11
SHANDONG NORMAL UNIV
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AI Technical Summary

Problems solved by technology

The matrix factorization model has a high dependence on the quality of the data, and its accuracy in large-scale data sets is poor; the traditional random walk algorithm only considers the correlation between the user-product vertices in the user-product bipartite graph when calculating the state transition but ignores the correlation between users
[0005] Recently, some scholars have brought the ranking idea into the recommendation system. The most widely used ranking-based recommendation model is the recommendation algorithm based on the pairwise model, a method that takes the partial order relationship as the optimization goal, but the current research work on pairwise There are the following problems: 1. High time complexity; 2. Poor interpretability of results; 3. Ignoring the weight of partial order relationship

Method used

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  • Product recommendation method and system combining pairwise optimization and matrix factorization
  • Product recommendation method and system combining pairwise optimization and matrix factorization
  • Product recommendation method and system combining pairwise optimization and matrix factorization

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Embodiment 1

[0031] Embodiment 1, this embodiment provides a product recommendation method combining pairwise optimization and matrix decomposition;

[0032] Product recommendation methods combining pairwise optimization and matrix factorization, including:

[0033] S1: Obtain the user's rating matrix for the product and the social relationship matrix between users;

[0034] S2: Cluster the product according to the user's rating matrix for the product, and divide the product into several clusters;

[0035] S3: Map the user's rating matrix to the product and the social relationship matrix between users into a weighted adjacency matrix;

[0036] S4: Define the user-product bipartite graph, use the random walk algorithm to fill the weighted adjacency matrix on the user-product bipartite graph, and obtain the probability matrix;

[0037] S5: Based on the clusters where the products are divided, establish an objective function based on pairwise optimization; perform matrix decomposition on th...

Embodiment 2

[0105] Embodiment 2, this embodiment provides a product recommendation system combining pairwise optimization and matrix decomposition;

[0106] A product recommendation system combining pairwise optimization and matrix factorization, including:

[0107] An acquisition module configured to acquire a user's rating matrix for a product and a social relationship matrix between users;

[0108] A clustering module, which is configured to cluster the product according to the rating matrix of the product by the user, and divide the product into several clusters;

[0109] The mapping module is configured to map the user's rating matrix to the product and the social relationship matrix between users into a weighted adjacency matrix;

[0110] The filling module is configured to define a user-product bipartite graph, and uses a random walk algorithm to fill the weighted adjacency matrix on the user-product bipartite graph to obtain a probability matrix;

[0111] The recommendation modu...

Embodiment 3

[0112] Embodiment 3: This embodiment also provides an electronic device, including a memory, a processor, and computer instructions stored in the memory and run on the processor. When the computer instructions are executed by the processor, each step in the method is completed. For the sake of brevity, the operation will not be repeated here.

[0113] Described electronic device can be mobile terminal and non-mobile terminal, and non-mobile terminal comprises desktop computer, and mobile terminal comprises smart phone (Smart Phone, such as Android mobile phone, IOS mobile phone etc.), smart glasses, smart watch, smart bracelet, tablet computer , laptops, personal digital assistants and other mobile Internet devices that can communicate wirelessly.

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Abstract

The present disclosure discloses a product recommendation method and system combining pairwise optimization and matrix decomposition to obtain user rating matrices for products and social relationship matrices between users; cluster products according to user rating matrices for products, and divide products into into several clusters; map the user-product rating matrix and the social relationship matrix between users into a weighted adjacency matrix; define a user-product bipartite graph, and use a random walk algorithm on the user-product bipartite graph to weighted adjacency The matrix is ​​filled to obtain the probability matrix; based on the clusters where the products are divided, an objective function based on pairwise optimization is established; the probability matrix is ​​decomposed based on the objective function, and the objective function value is minimized to obtain the decomposed matrix and products Recommended list.

Description

technical field [0001] The present disclosure relates to the technical field of product personalized recommendation, in particular to a product recommendation method and system combining pairwise optimization and matrix decomposition. Background technique [0002] The statements in this section merely mention background art related to the present disclosure and do not necessarily constitute prior art. [0003] In the process of realizing the present disclosure, the inventors found that the following technical problems existed in the prior art: [0004] The recommendation algorithm based on collaborative filtering is currently the most popular recommendation algorithm, and has been favored by many researchers because of its strong compatibility in various fields and considerable recommendation performance. Currently, collaborative filtering algorithms have two popular research directions: 1. Latent Factor Model 2. Graph-based Model. The representative method of hidden facto...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9535G06F16/9536G06F17/16G06K9/62
CPCG06F16/9535G06F16/9536G06F17/16G06F18/23
Inventor 冯珊珊姜润青徐誉畅
Owner SHANDONG NORMAL UNIV
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