|Table of Contents|

Collaborative Filtering Method Based on User Rating Subspace and Trust Mechanism for Recommendation System(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2023年03期
Page:
27-35
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Collaborative Filtering Method Based on User Rating Subspace and Trust Mechanism for Recommendation System
Author(s):
Zhang Li; Sun Xiaohan; Zheng Xiaohan
(School of Computer Science and Technology, Soochow University, Suzhou 215006, China)
Keywords:
recommendation system;  collaborative filtering;  trust mechanism;  user rating subspaces;  iterative rating prediction
PACS:
TP391
DOI:
10.3969/j.issn.1672-1292.2023.03.004
Abstract:
The rapid growth of the internet has led to the explosive growth of information on the internet. To solve the issue of information overload, recommendation system has been proposed. The core idea behind recommendation system is to explore users' personalized preferences based on users' historical behavior data, and recommend items that match users' interests to users. However, sparse rating data leads to poor accurate similarity calculations, which in turn affect the quality of similar user sets. To improve the reliability of similar user set, this paper proposes a collaborative filtering algorithm based on user rating subspace and trust mechanism(URSTM)for recommendation system. The innovation of this paper contains the following two main points. Firstly, URSTM introduces a trust mechanism constructed based on a trust relationship explicitly declared by users, which can supplement the sparse rating data. Secondly, by using the rating subspace and trust relationship, this paper designs a hybrid similarity measurement based on explicit and implicit similarities, and then integrates it into the multi-order nearest neighbor search method and iterative rating prediction method. Experimental results show that URSTM can improve the accuracy of recommendation performance and has a better prediction ability.

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Last Update: 2023-09-15