Kernel method-based collaborative filtering recommendation system and method

Abstract

The invention provides a kernel method-based collaborative filtering recommendation system and a kernel method-based collaborative filtering recommendation method. The corresponding system comprises a data preparation module which is used for standardizing the original data and carrying out corresponding preprocessing, generating a user-project rating matrix and a project distance matrix to output; a user interest modeling module which is used for constructing an interest model for a user on a project space according to the user-project rating matrix and the project distance matrix as well as a kernel density estimation technology; and a recommendation result generation module which is used for computing the similarities among the users according to the interest model, generating a neighbor set of a target user, and predicting a score of the project rated by the user according to a predetermined recommendation strategy and returning the recommendation result. Through the recommendation system and the recommendation method provided by the invention, the user interest model can be better presented, the user similarity in the practical application is estimated more accurately, the performance of the recommendation system can be promoted considerably, and more stable recommendation result can be obtained.

Description

technical field [0001] The invention belongs to the field of Internet data mining and information retrieval, and relates to a product recommendation system and method for e-commerce websites. Background technique [0002] With the rapid development of information technology and WEB 2.0 technology, Internet information is becoming increasingly large and maintaining rapid growth. For Internet users, the problem to be solved is how to efficiently and quickly mine valuable information from massive amounts of information; and for some social networking sites, e-commerce sites and other sites, it is even more important to consider how to effectively present website content to users to improve service quality. Personalized recommendation technology is gradually developed under such a background. The main idea of ​​this technology is to establish an interest model describing user needs by mining the user's historical behavior records, and then use the interest model to discover use...

AI Technical Summary

Problems solved by technology

[0004] The traditional collaborative filtering recommendation algorithm needs to consider the common disadvantages of various collaborative filtering recommendation algorithms, mainly including: First, the traditional similarity measurement method only considers the data with common ratings when calculating the similarity between items or users. As a result, only users with items with common ratings are likely to be similar, which is inconsistent with the actual situation; second, collaborative recommendation faces the challenges of data sparsity and cold start problems. In the case of sparse rating data, how to reasonably calculate The similarity between them, and then producing accurate recommendation results has become a key issue in improving the quality of collaborative filtering recommendation algorithms.

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