Multi-feature Collaborative Filtering Recommendation for Sparse Dataset

Collaborative filtering algorithms become losing its effectiveness on case that the dataset is sparse. When user ratings are scared, it's difficult to find real similar users, which causes performance reduction of the algorithm. We here present a 3-dimension collaborative filtering framework which can use features of users and items for similarity computation to deal with the data sparsity problem. It uses feature and rating combinations instead of only ratings in collaborative filtering process and performs a more complete similarity computation. Specifically, we provide a weighted feature form and a Bayesian form in its implementation. The results demonstrate that our methods can obviously improve the performance of collaborative filtering when datasets are sparse.