Conceptual collaborative filtering recommendation: A probabilistic learning approach

Collaborative filtering is one of the most successful and popular methods in developing recommendation systems. However, conventional collaborative filtering methods suffer from item sparsity and new item problems. In this paper, we propose a probabilistic learning approach that solves the item sparsity problem while describing users and items with domain concepts. Our method uses a probabilistic match with domain concepts, whereas conventional collaborative filtering uses an exact match to find similar users. Empirical experiments show that our method outperforms the conventional ones.