An e-commerce recommendation method for tracking user interest changes

Abstract

An e-commerce recommendation method of tracking user interest changes is provided. Based on historical data of a user, the following operations are carried out sequentially: first, in view of a problem that a rating matrix is sparse in an e-commerce recommendation system, a user page retention time and other implicit feedbacks are used to obtain an implicit rating, an explicit rating is also use to construct a "user - commodity" comprehensive rating matrix which reflects a user preference; second, according to a rating record of the user in a recent period of time, an existing commodity category information is used to obtain a commodity category information similarity between users; and finally, a rating similarity and a commodity category information similarity between the users are comprehensively considered, and a collaborative filtering algorithm based on a time weight is used to recommend a most interesting commodity to the user. The advantages of the invention are that: the dynamic changes in the user interest are taken into the full consideration; and the commodity category information and the implicit feedback of the user are effectively used to recommend the commodity which is more in line with the current demand of the user.

Description

technical field [0001] The invention relates to the technical field of e-commerce recommendation systems, in particular to an e-commerce recommendation method for tracking user interest changes. Background technique [0002] Personalized recommendation technology mainly analyzes the behavior of different users, guesses user interests, and actively recommends resources to users, thereby alleviating the contradiction between the Internet information explosion and users' rapid acquisition of information, and also making up for the weak ability of general search engines to provide personalized feedback results. shortcoming. At present, recommendation technology is widely used in practical systems such as e-commerce, scientific literature retrieval, online music websites and digital libraries. [0003] In recent years, the emergence of e-commerce has brought about a revolutionary change in commodity circulation. First, the range of choices available to consumers has been greatly...

AI Technical Summary

Problems solved by technology

However, the classic collaborative filtering algorithm cannot reflect the migration of user interests over time in a timely manner. If the timeliness of product ratings and product category information can be fully utilized, the recommendation effect will be further improved.

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