Personalized Recommendation of Online Shopping Products Based on Online Fast Learning through Latent Factor Model

Abstract

In order to improve the personalized recommendation effect of online shopping products, this article combines online fast learning through latent factor model to construct a personalized virtual planning recommendation system for online shopping products. Moreover, this article improves on the ONMTF model. In the problem of cross-domain recommendation, this article clusters users and items in each data domain with hidden scoring patterns and learns common scoring patterns that can be shared between different data domains to deal with the data sparse problem that often occurs in recommender systems. The experimental research results show that cross-domain recommendation can indeed use the implicit semantics or topics between domains to share information and knowledge, thereby improving the accuracy of recommendation.