Relation regularized subspace recommending for related scientific articles

Recommending related scientific articles for a researcher is very important and useful in practice but also is full of challenges due to the latent complex semantic relations among scientific literatures. To deal with these challenges, this paper proposes a novel framework with link-missing data adaption, which casts the recommendation task to subspace embedding and similarity ranking problems. The relation regularized subspace in this framework is constructed via Relation Regularized Matrix Factorization (RRMF) for well modeling both content and link structure simultaneously. However, the link structure for an article is not always available in practical recommending. To solve this problem, we further propose two alternative approaches based on Latent Dirichlet Allocation (LDA) for link-missing articles recommendation as an extension of RRMF. Experiments on CiteSeer dataset demonstrate our method is more effective in comparison with some state-of-the-art approaches and is able to handle the link-missing case which the link-based methods never can fit.