A hybrid collaborative filtering recommendation algorithm: integrating content information and matrix factorisation. (24th April 2020)

• Record Type: Journal Article
• Authors: Wang, Jing; Sangaiah, Arun Kumar; Liu, Wei
• Is Part Of: International journal of grid and utility computing. Volume 11:Number 3(2020)
• Page Start: 367
• Page End: 377
• Publication Date: 2020-04-24
• Subjects: recommender system -- collaborative filtering -- matrix factorisation -- momentum stochastic gradient descent; Electronic data processing -- Distributed processing -- Periodicals; Electronic commerce -- Management -- Computer programs -- Periodicals; 004.605
• Languages: English
• ISSNs: 1741-847X

Abstract

Matrix factorisation is a one of the most popular techniques in recommendation systems. However, matrix factorisation still suffers from cold start problem and needs complicated computation. In this paper, we present a hybrid recommendation algorithm, which integrates user and item content information and matrix factorisation. First, based on user or item content information, biases of user or item can be evaluated in advance. Incorporating user and item biases into matrix factorisation model, we can obtain final prediction model. At last, momentum stochastic gradient descent method is used to optimise other parameters. Experimental results on a real data set have shown best performance of our algorithm in terms of MAE and RMSE when compared with other classical matrix factorisation recommendation algorithms.