Joint Matrix Factorization: A Novel Approach for Recommender System

Shaolun Sun,Yuetong Xiao,Xiaoli Su,Yue Huang,Wendong Xiao,Sen Zhang,Heng Zheng

doi:10.1109/access.2020.3044046

Abstract

Collaborative filtering (CF) is the most classical method for recommender system, but it is usually suffered from limited performance by the sparseness of user-to-item rating data. Recently, due to the powerful learning feature representation ability, deep learning components are used to leverage auxiliary information to assist recommendation. However, most existing models based on deep learning are incomplete so that merely extracting the item latent representation and ignoring the user parts. Besides, different data are not chosen from current models. This paper proposes a novel probability framework, named as joint matrix factorization (JMF). There are three components in JMF. Firstly, the modified multilayer crossing version of the factorization machine (MFM) is designed to extract the user latent factors based on user behavior information. Moreover, MFM is a general method which can be used to accomplish many tasks in terms of machine learning. Secondly, a modification of Long Short-Term Memory (LSTM), named as bidirectional LSTM (BLSTM), is used to extract the item latent factors of a document sequence from both front and back directions. Finally, we tightly integrate BLSTM and MFM into probabilistic matrix factorization (PMF) to form JMF. Compared with the classical matrix factorization and other integration models, JMF extracts document data as well as user behavioral data as item vectors and user vectors. Extensive experiments on five real-world datasets show the proposed model has better performance compared with the state-of-the-art recommendation methods.

Highlights

With the rapid development of the internet, we are surrounded by all kinds of information
For traditional collaborative filtering-based method (CF) methods, joint matrix factorization (JMF) improves signifycantly than BiasedSVD and BPMF, which indicates that the use of deep learning components to extract auxiliary information into matrix factorization can improve prediction accuracy effectively
For the factorization machine model, deepFM has a good performance on five data sets, it uses deep learning components

Summary

Introduction

With the rapid development of the internet, we are surrounded by all kinds of information. In order to help people , make effective decisions in the case of information overload, there are two main options: Search Engine and Recommender System. Previous search engines merely returned information with high similarity according to input query. Current search engines incorporate the idea of recommendation, i.e., combining user’s various preferences for personalization search. By analyzing the user’s preferences and the item’s attributes, the recommender system can actively offer personalized suggestions for users. The classical recommendation algorithm [1] is roughly divided into three categories: content-based method, collaborative filtering-based method (CF) [2,3] and hybrid method. CF has been widely used and includes neighborhood-based CF and model-based CF. Matrix factorization (MF), as a representative of model-based CF, represents users and items by automatically learning latent factors.

Objectives

Methods

Results

Conclusion