Cross-domain recommendation method fusing label and attention mechanism and implementation system thereof

Abstract

The invention discloses a cross-domain recommendation method fusing label and attention mechanism and an implementation system thereof, and the recommendation method comprises the steps: firstly, selecting and constructing a cross-domain fused label, and respectively carrying out the weighted summation of label vectors of a source domain and a target domain to obtain a resource vector; secondly, according to an interest mining algorithm based on an attention mechanism, obtaining preferences of the user in a source field and a target field; thirdly, learning label mapping between a source domain and a target domain according to a BP neural network-based cross-domain label mapping algorithm, and obtaining comprehensive preferences of the user in the target domain; and finally, recommending projects with high similarity to the comprehensive preferences of the users in the target domain to the users through a cross-domain recommendation algorithm fusing label mapping and attention mechanisms. The preferences of the user in different fields are comprehensively considered through cross-field recommendation, so that the cold start problem of the user in target field recommendation is improved; and meanwhile, in a cross-domain recommendation system, by analyzing the preferences of the user in different domains, the recommendation results are more diversified.

Description

technical field [0001] The invention relates to the technical field of information recommendation methods and systems, in particular to a cross-domain recommendation method and an implementation system that integrates tags and attention mechanisms. Background technique [0002] With the rapid development of Internet technology, the number of various social application software such as QQ, WeChat, and Weibo has increased rapidly, and a variety of information is presented to people, which greatly enriches people's daily life. However, some inevitable problems appeared in this process, such as information flood and information stray. In order to help each user obtain resources better, personalized recommendation technology emerges as the times require. Currently, relevant researchers apply personalized recommendation technology to resource recommendations in various fields, including not only movies, music, and sports, but also e-commerce, location-based services, and medical ...

AI Technical Summary

Problems solved by technology

[0004] At present, most of the recommendation technologies are single-field recommendation technologies, that is, only use the user's interest in a single field to recommend users, and there are few recommendation technologies that combine multiple fields.

In single-domain recommendation, there are often problems such as data sparseness, user cold start, and product cold start, which make the performance of the recommendation system decrease and the accuracy of the recommendation decrease.

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