A Low-Rank Tensor Factorization Using Implicit Similarity in Trust Relationships

Pei Ma,Liejun Wang,Jiwei Qin

doi:10.3390/sym12030439

Abstract

Low-rank tensor factorization can not only mine the implicit relationships between data but also fill in the missing data when working with complex data. Compared with the traditional collaborative filtering (CF) algorithm, the changes are essentially proposed, from traditional matrix analysis to three-dimensional spatial analysis. Based on low-rank tensor factorization, this paper proposes a recommendation model that comprehensively considers local information and global information, in other words, combining the similarity between trust users and low-rank tensor factorization. First, the similarity between trusted users is measured to capture local information between users by trusting similar preferences of users when selecting items. Then, the users’ similarity is integrated into the tensor, and the low-rank tensor factorization is used to better maintain and describe the internal structure of the data to obtain global information. Furthermore, based on the idea of the alternating least squares method, the conjugate gradient (CG) optimization algorithm for the model of this paper is designed. The local and global information is used to generate the optimal expected result in an iterative process. Finally, we conducted a large number of comparative experiments on the Ciao dataset and the FilmTrust dataset. Experimental results show that the algorithm has less precision loss under the data set with lower density. Thus, not only can a perfect compromise between accuracy and coverage be achieved, but also the computational complexity can be reduced to meet the need for real-time results.

Highlights

In a network environment in which data amounts are soaring, the traditional ‘resource retrieval’ method on the Internet has long been unable to meet the needs of users [1]
This paper first analyzes the user’s social network relationships and context information, and it proposes a low-rank tensor model recommendation method based on the implicit similarity of trust users for sparse and cold-start problems
Our method utilizes local and global information to effectively improve the prior conditions of the model, which can improve the reliability of the recommendation, and alleviate the user’s cold start problem

Summary

Introduction

In a network environment in which data amounts are soaring, the traditional ‘resource retrieval’ method on the Internet has long been unable to meet the needs of users [1]. The best method of Internet information dissemination is to automatically present information to users according to their choices. The recommendation system has come into being [2]. It can sort certain items based on certain strategic specifications, and display the items listed in the forefront to the user, which facilitates the user’s choice. The recommendation of information consultation provides convenience for the users, and brings enormous benefits to the Internet industry. The recommendation algorithm has great research value as the core [3]

Methods

Results

Conclusion