Goods recommendation method based on scores and user behaviors

Abstract

The invention discloses a goods recommendation method based on scores and user behaviors. First of all, a latent factor model is established for user score data, goods are automatically clustered, latent classes or feature factors are found, user interest is decomposed into preference degrees of the multiple latent classes, the goods are expressed by use of weights comprising latent features, and the scores of the users for the goods are inner products of the user interest and the goods. Then for the purpose of solving the score data sparsity problem, by use of the user behaviors, negative samples are introduced, the features are extracted, and a possibility that the users buy the goods is estimated through a logic regression model. Finally, candidate sets of the two are combined and weighed for ordering, and top goods are recommended to the users. According to the invention, diversified interest of the users is discovered from the single scores by use of the latent factor model, information of the multiple features of the goods is mined, the method better accords with actual application, the negative samples are introduced, distinctiveness of the user interest is enabled to be larger, the quality of a recommendation result is higher, demands of the users can be better satisfied, and the method can be applied to recommending the goods.

Description

technical field [0001] The invention belongs to the technical field of data mining, and in particular relates to a recommendation system technology. Background technique [0002] With the rapid development of the Internet and e-commerce, commodity information has exploded, and consumers are trapped in massive amounts of information, making it difficult to make purchase decisions quickly and effectively. In order to allow customers to browse as little irrelevant information as possible when purchasing products and improve the system’s ability to mine long-tail products, a recommendation system emerged as the times require. It can not only provide personalized recommendations, improve user loyalty, but also increase purchase conversion rates and increase sales volume. Collaborative filtering is the most widely used algorithm in the recommendation system. It is mainly divided into memory-based (Memory_based) and model-based (Model_based). In the memory-based recommendation alg...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a product recommendation method based on ratings and user behaviors to solve the problems of data sparseness and lack of diversification of user interests, improve the accuracy of user interest models, and improve the accuracy of recommendation

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