Cross-domain recommendation method based on user preference personalized migration

Abstract

The invention discloses a user preference personalized migration-based cross-domain recommendation method, which comprises the following steps of: screening overlapped users and commodities from an auxiliary domain and a target domain, and obtaining user preferences and commodity characteristics by using an aspect-level user preference algorithm; an inter-domain user sharing migration module is constructed by using a generative adversarial network, and the problem of data distribution difference between a target domain and an auxiliary domain is solved; and different personalized mapping functions are generated for the users by using a meta-learner, so that the problem that a shared migration module is difficult to express a complex relationship between the auxiliary domain and the target domain is solved, and the final user preference after the target domain is migrated is obtained. According to the method, user preferences of the auxiliary domain are migrated to the target domain, the problem that a traditional recommendation method fails under the condition of user cold start due to data sparseness in the target domain can be solved, and a good recommendation effect is achieved.

Description

technical field [0001] The present invention relates to the technical field of recommendation systems, in particular to a cross-domain recommendation method based on personalized migration of user preferences. Background technique [0002] In the information age, the recommendation system serves as an effective filtering method to screen out the items that users may be interested in among a large amount of information, which can improve the user experience, thereby promoting the further development of the network platform, increasing user stickiness, and forming a virtuous positive cycle. . The traditional recommendation system is divided into ideas based on collaborative filtering, content-based and hybrid recommendation. The core idea is to recommend similar products to users based on the user’s historical behavior; recommend. Traditional recommendation algorithms can alleviate the problem of information explosion on e-commerce platforms to a certain extent, but their de...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that the traditional recommendation method fails in the case of user cold start due to data sparseness in the recommendation system, and proposes a cross-domain recommendation method based on user preference personalized migration. The data is encoded into the same feature space to solve the problem of data distribution differences between the auxiliary domain and the target domain, and realize the sharing and migration of users between domains; The complex mapping relationship between domains realizes the personalized migration of user preferences on the basis of shared migration between domains, which can further enhance the effective use of auxiliary domain knowledge, thereby alleviating the problem of data sparsity in the target domain and improving the accuracy of user rating predictions for products. thereby improving the recommendation effect

Images