Personalized recommendation method based on commodity property entropy

Abstract

The invention relates to a personalized recommendation method based on commodity property entropy. The method comprises the following steps of: achieving the browsing history of electronic commerce website users through a script; analyzing the browsing history; and generating a recommendation result and performing personalized recommendation. In the method, the user profile based on the commodity property entropy can help the recommendation calculation method to find the preference of the users in different property classification, and different recommendation is generated by utilizing the information of the users in the browsing process and according to the actual selection of the users, so that diversity of the commodity page recommendation is improved. Based on a recommendation system requiring personalized recommendation, the hit rate of recommendation is maintained and recommendation diversity is improved. By the method, on the premise of not changing greatly, the results generated by other recommendation calculation methods are sequenced again, so that the effect of the original recommendation calculation method is not influenced, diversity of the commodity page personalized recommendation is improved, and recommendation on the commodity pages is more effective.

Description

technical field [0001] The invention belongs to the field of recommendation algorithms, especially the field of personalized recommendation of recommendation algorithms, which is used for all recommendation algorithms capable of generating recommendation results, and is a recommendation result optimization technology. Background technique [0002] Personalized recommendation is to recommend information and products that the user is interested in based on the user's interest characteristics and purchase behavior. With the continuous expansion of the scale of e-commerce and the rapid growth of the number and types of commodities, customers need to spend a lot of time to find the commodities they want to buy. This process of browsing a large amount of irrelevant information and products will undoubtedly lead to the continuous loss of consumers who are submerged in the problem of information overload. In order to solve these problems, personalized recommendation system came int...

AI Technical Summary

Problems solved by technology

However, due to the increasing number of product attributes, the user profile based on product attributes has become increasingly large. If a method is not used to improve the original simple statistics, the efficiency of content recommendation will eventually be low.

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