Latent Path Connected Space Model for Recommendation

Lang Mei, Jun He, Hongyan Liu, Xiaoyong Du

Abstract: Matrix Factorization (MF) is a latent factor model, which has been one of the most popular techniques for recommendation systems. Performance of MF-based recommender models degrades as the sparseness of user-item rating data increases. MF-based models map each user and each item into a low dimensional space, where either of them is represented by a point in the space. While a point is a concise and simple representation of a user’s preference or an item’s characteristics, it is hard to learn the precise position of the point, especially when the data is very sparse. In this paper we propose an alternative latent space model, Latent Path Connected Space model (LSpace), to address this issue. In this model, users and items are both represented by path connected space described by different latent dimensions and spatial intersection between user space and item space reflects their matching degree. Extensive evaluations on four real-world datasets show that our approach outperforms the Matrix Factorization model on rating prediction task especially when the rating data is extremely sparse.