Personalized recommendation by matrix co-factorization with tags and time information

Abstract

Personalized recommendation systems have solved the information overload problem caused by large volumes of Web data effectively. However, most existing recommendation algorithms are weak in handling the problem of rating data sparsity that characterizes most recommender systems and results in deteriorated recommendation accuracy. The results in the KDDCUP and Netflix competition have proven that the matrix factorization algorithm achieves better performance than other recommendation algorithms when the rating data is scarce. However, the highly sparse rating matrix will cause the overfitting problem in matrix factorization. Although regularization can relieve the issue of overfitting to some extent, it is still a significant challenge to train an effective model for recommender systems when the data is highly sparse. Therefore, this paper proposes a co-SVD model to enrich the single data source and mitigate the overfitting problem in matrix factorization. The user preferences are enriched not only by rating data but also the tag data; subsequently, the relevance between tags and item features are explored. Furthermore, according to the assumption that user preferences will change with time, we optimize the preference and relevance by adding the temporal influence. Based on the MovieLens benchmark datasets, the experimental results indicate that the proposed co-SVD method is more effective than other baselines. Matrix co-factorization provides an effective method to the solve data sparsity problem with additional information. The method can be used to address this problem in various expert and intelligent systems such as recommendation advertisements, e-commerce sites, and social media platforms, all of which require a relatively large amount of input data from users.