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

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...

Full description

Saved in:
Bibliographic Details
Main Author: Meng Shi
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Advances in Multimedia
Online Access:http://dx.doi.org/10.1155/2022/9292874
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1687-5699