Scientific Articles Recommendation

We study the problem of recommending scientific articles to users in an online community and present a novel matrix factorization model, the topic regression Matrix Factorization (tr-MF), to solve the problem. The main idea of tr-MF lies in extending the matrix factorization with a probabilistic topic modeling. Instead of regularizing item factors through the probabilistic topic modeling as in the framework of the CTR model, tr-MF introduces a regression model to regularize user factors through the probabilistic topic modeling under the basic hypothesis that users share the similar preferences if they rate similar sets of items. Consequently, tr-MF provides interpretable latent factors for users and items, and makes accurate predictions for community users. Specifically, it is effective in making predictions for users with only few ratings or even no ratings, and supports tasks that are specific to a certain field, neither of which is addressed in the existing literature. Further, we demonstrate the efficacy of tr-MF on a large subset of the data from CiteULike, a bibliography sharing service dataset. The proposed model outperforms the state-of-the-art matrix factorization models with a significant margin.