Commodity recommendation optimizing method and system based on collaborative filtering

Abstract

The invention provides a commodity recommendation optimizing method based on collaborative filtering. The commodity recommendation optimizing method comprises the steps that tag types corresponding to commodities are set according to received setting instructions; the internet behavior data information of a user is acquired, and the attention value of the user on each commodity in the commodities is determined according to the internet behavior data information; the tag types corresponding to the commodities are determined to determine the values of the attention of the user to the tag types corresponding to the commodities; according to the attention values, a user recommendation group which the user belongs to is determined; commodity recommendation is carried out on the user according to the attention values of the other users in the recommendation group on the commodities. Correspondingly, the invention further provides a commodity recommendation optimizing system based on collaborative filtering. According to the technical scheme, tags are introduced in the commodity recommendation optimizing method based on collaborative filtering, due to the fact that the number of the tags is far smaller than the number of the commodities, calculation of the pertinence of the user and the commodities is greatly reduced, the calculation quantity is reduced, and meanwhile the recommendation accuracy is guaranteed.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a collaborative filtering-based commodity recommendation optimization method and a collaborative filtering-based commodity recommendation optimization system. Background technique [0002] With the development of information technology and the Internet, in this era of information overload, both information consumers and information producers have encountered great challenges. For information consumers, it is very difficult to find the information they are interested in from a large amount of information; for information producers, it is also very difficult to make the information they produce stand out and attract the attention of the majority of users things. [0003] As an important tool to solve this contradiction, the recommendation system’s task is to connect information consumers and information producers. On the one hand, it helps information consumers find info...

AI Technical Summary

Problems solved by technology

However, most of the existing recommendation methods are based on the cosine theorem to complete the calculation of the user's attention to the product. For the business system with a huge amount of data, the amount of calculation is too

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