Collaborative filtering recommending method and system based on client characteristics

Abstract

The invention discloses collaborative filtering recommending method and system based on client characteristics. The method comprises the following steps of: obtaining user information and carrying out information expression, neighbor formation and recommendation generation. Based on a most adjacent collaborative filtering algorithm, the method comprises algorithm name, algorithm input and algorithm output, and also comprises algorithm analysis. The invention also discloses a collaborative filtering recommending system based on client characteristic forecasting, which comprises data loading, recommendation engine as well as client mail notification and recommendation daemon process.

Description

technical field [0001] The invention relates to the technical field of computer networks, in particular to a collaborative filtering recommendation method and system based on customer characteristics. Background technique [0002] With the popularization of the Internet and the development of e-commerce, recommendation systems, especially personalized recommendations, have gradually become an important research content of e-commerce technology and have attracted more and more researchers' attention. At present, almost all large-scale e-commerce systems, such as Amazon, CDNOW, eBay, Dangdang online bookstore, etc., have used various forms of recommendation systems to varying degrees. [0003] A recommendation system is a system that uses statistics and knowledge discovery techniques to solve the problem of providing product recommendations when interacting with target customers. It provides product information and suggestions to customers in the e-commerce system, helps cust...

AI Technical Summary

Problems solved by technology

[0006] With the further expansion of the scale of the e-commerce system, the number of users and item data have increased sharply, resulting in the extreme sparsity of user rating data. In the case of extremely sparse user rating data, various methods of traditional similarity measurement have their own drawbacks. , making the calculated nearest neighbors of the target user inaccurate, and the recommendation quality of the recommendation system drops sharply

More importantly, these algorithms are only applicable on the premise of user rating records. For e-commerce systems without user ratings, collaborative filtering technology cannot be widely used in e-commerce recommendation systems.

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