Cross user-based cross-domain recommendation method

Abstract

The invention belongs to the technical field of the internet and discloses a cross user-based cross-domain recommendation method. The cross user-based cross-domain recommendation method comprises the steps of processing data of a source domain and a target domain, screening out cross users, and fusing the data, in the two domains, of the cross users to form a new scoring matrix; and then performing normalization processing on scores, and performing calculation by using a user similarity calculation formula. According to the method, user similarity can be calculated by completely depending on data of an original domain, and finally recommendation is generated, so that the cold-start problem of the users is solved to a certain extent; an improved user similarity calculation formula of the cross users is proposed, so that a new cross-domain recommendation algorithm is formed; and according to the cross user-based cross-domain recommendation algorithm, the data, in the source domain, of the cross users is migrated to the target domain, and data richness of the target domain is enhanced, thereby obtaining a more accurate personalized recommendation result.

Description

technical field [0001] The invention belongs to the technical field of the Internet, and in particular relates to a cross-user-based cross-domain recommendation method. Background technique [0002] With the development of information technology, the Internet has penetrated into all aspects of life, and we are in a prosperous and developing Internet era. There is a huge amount of information on the Internet today. Every day, people get complicated information from mobile phones, computers, TVs, smart watches, large screens at stations, and elevator building screens, and they have to choose the parts that are useful to them. Too much information, but too little available or interesting information, this phenomenon is called "information overload". In order to cope with and solve this problem, major commercial companies and scientific research institutions have proposed effective methods, such as portal websites, which classify and list information on web pages, and users can...

AI Technical Summary

Problems solved by technology

[0003] To sum up, the problems existing in the existing technology are: the traditional personalized recommendation system only recommends in a single field, resulting in cold start and data sparsity; the user-item matrix used for collaborative filtering becomes very large and very complex. sparse

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