user

sequential recommendation

recommender systems

user item

collaborative filtering

reputation systems

Decision Support Systems

An important factor affecting the performance of collaborative filtering for recommendation systems is the sparsity of the rating matrix caused by insufficient rating data. Improving the recommendation model and introducing side information are two main research approaches to address the problem. We combine these two approaches and propose the Review-Based Matrix Factorization method in this paper. The method consists of two phases. The first phase is review-based collaborative filtering, where an item-topic rating matrix is constructed by the feature-level opinion mining of online review text. This rating matrix is used to derive item similarities, which can be used to infer unknown users' ratings of the items. The second phase consists of rating imputation, where we first fill some of the empty elements of the user-item rating matrix, then conduct matrix factorization to learn the latent user and item factors to generate recommendations. Experiments on two actual datasets show that our method improves the accuracy of recommendation compared with similar algorithms. • Collaborative filtering suffers from the sparsity issue. • The proposed method helps solve the overfitting problem of matrix factorization when rating data is extremely sparse. • The Review-based Matrix Factorization framework gives a clear path for using review text in collaborative filtering. • The proposed solution solves the sparsity problem from both model and data perspectives.

Combining review-based collaborative filtering and matrix factorization: A solution to rating's sparsity problem