Cross-domain commodity cold start recommendation method and device and electronic equipment

Abstract

The embodiment of the invention provides a Cross-domain commodity cold start recommendation method and device and electronic equipment. The method comprises the following steps: acquiring visual information of a visiting customer; extracting a source label in the visual information, wherein the source label has a source domain attribute; according to the source tag, obtaining a target tag associated with the source tag, the target tag having a target domain attribute, and the source domain and the target domain belonging to different domains; and recommending a target commodity corresponding to the target tag to the visiting customer. On the premise of not depending on user behavior data, the label relation can be efficiently mined, and therefore the cross-domain commodity recommendation effect is improved.

Description

technical field [0001] The present invention relates to the field of cross-domain commodity recommendation, in particular to a cold-start recommendation method, device and electronic equipment for cross-field commodities. Background technique [0002] In the era of product diversification, in order to improve shopping services, merchants have implemented a variety of shopping service methods, such as product recommendation services. In the existing label-based recommendation system, it is usually to extract the label features of the products purchased by the visiting customers, and then use these labels to find products with similar labels for recommendation. Types of products, such as buying programming books, will recommend related books in the programming field, and the recommendation effect is poor. There are also methods that try to model the purchasing behavior of visiting customers in two different fields, extract the behavioral characteristics of visiting customers,...

AI Technical Summary

Problems solved by technology

Then, in a recommendation system, if we can only obtain the label information of visiting customers in a certain field, but when we want to recommend products in another field, since there is no behavior data of visiting customers in another field in the recommendation system, we cannot Recommend products in another field to visiting customers, resulting in poor product recommendation effect of the recommendation system

It can be seen that the existing cross-domain product recommendation methods have the problem of poor recommendation effect

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