Cross-domain recommendation data processing method with multiple auxiliary domains and cross-domain recommendation system

Abstract

The invention belongs to the technical field of e-commerce information processing, and discloses a cross-domain recommendation data processing method with multiple auxiliary domains and a cross-domainrecommendation data processing system. The method comprises obtaining a scoring matrix of an auxiliary domain, calculating the scoring reliability of a user, carrying out equal-proportion segmented mapping on a threshold value, and emptying scores with the number of scores lower than the threshold value in the auxiliary domain; obtaining clustering level scoring matrix of all domains by using a K-means clustering algorithm, and carrying out matrix decomposition; meanwhile, decomposing the target domain scoring matrix to learn a feature mapping function for the cold start user; evaluating predicted scoring matrix by using an average absolute error. Compared with the prior art, the method has the advantages that K-means clustering algorithm is used in the data processing process for obtaining clustering-level user project scoring matrix combined with all domains, the data sparsity of the cold start user is reduced. The problem that the recommendation effect is not ideal due to poor prediction accuracy of a traditional single auxiliary domain matrix factorization model is solved, the recommendation effect of the recommendation system is improved, and the method has higher universality.

Description

technical field [0001] The invention belongs to the technical field of e-commerce information processing, and in particular relates to a cross-domain recommendation data processing method with multiple auxiliary domains and a cross-domain recommendation data processing system. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: With the rapid development of Internet technology and Web technology, e-commerce has become a new form of business activities, and more and more consumers are willing to purchase the commodities they need through the Internet. Through online shopping, consumers can browse a variety of products; compared with traditional offline shopping, online shopping enables consumers to have more choices. But on the other hand, the fast update speed of all kinds of information on the Internet makes it easy for consumers to get lost in a wide variety of "product oceans". While bringing many conveniences...

AI Technical Summary

Problems solved by technology

[0005] To sum up, the problems existing in the existing technology are: the existing technology is aimed at the cold-start user problem in the target domain when there are anchor users in the two domains, and cannot make cross-domain recommendations for the case of multiple auxiliary domains

Second, users have different preferences and behavioral characteristics in different fields, and the scoring data in different fields cannot be simply mixed

Third, although the preferences and characteristics of the same user in different domains are related, they also vary greatly

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