Collaborative filtering using orthogonal nonnegative matrix tri-factorization

Collaborative filtering aims at predicting a test user’s ratings for new items by integrating other like-minded users’ rating information. The key assumption is that users sharing the same ratings on past items tend to agree on new items. Traditional collaborative filtering methods can mainly be divided into two classes: memory-based and model-based. The memory-based approaches generally suffer from two fundamental problems: sparsity and scalability, and the model-based approaches usually cost too much on establishing a model and have many parameters to be tuned. In this paper, we propose a novel framework for collaborative filtering by applying orthogonal nonnegative matrix tri-factorization (ONMTF), which (1) alleviates the sparsity problem via matrix factorization; (2) solves the scalability problem by simultaneously clustering rows and columns of the user-item matrix. Experiments on the benchmark data set show that our algorithm is indeed more tolerant against both sparsity and scalability, and achieves good performance in the mean time.