Content-Enhanced Matrix Factorization for Recommender Systems

Abstract:

Recommender systems are widely employed in many fields to recommend products, services and information to potential customers. As the most successful approach to recommender systems, collaborative filtering (CF) predicts user preferences in item selection based on the known user ratings of items. It can be divided into two main braches - the neighbourhood approach (NB) and latent factor models. Some of the most successful realizations of latent factor models are based on matrix factorization (MF). Accuracy is one of the most important measurement criteria for recommender systems. In this paper, to improve accuracy, we propose an improved MF model. In this model, we not only consider the latent factors describing the user and item, but also incorporate content information directly into MF.Experiments are performed on the Movielens dataset to compare the present approach with the other method. The experiment results indicate that the proposed approach can remarkably improve the recommendation quality.

• Periodical: Applied Mechanics and Materials (Volumes 475-476)
• Pages: 1084-1089
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.475-476.1084
• Online since: December 2013
• Authors: Hui Yuan Chang, Ding Xia Li*, Qi Dong Liu, Rong Jing Hu, Rui Sheng Zhang
• Keywords: Collaborative Filtering (CF), Content Information, Matrix Factorization, Recommender System

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