A probabilistic approach to semantic collaborative filtering using world knowledge

Collaborative filtering, which is a popular approach for developing recommendation systems, exploits the exact match of items that users have accessed. If the users access different items, they are considered as unlike-minded users even though they may actually be semantically like-minded. To solve this problem, we propose a semantic collaborative filtering model that represents the semantics of users' preferences and items with their corresponding concepts. In this work, we extend the Bayesian belief network (BBN)-based model because it provides a clear formalism for representing users' preferences and items with concepts. Because the conventional BBN-based model regards the index terms derived from items as concepts, it does not exploit domain knowledge. We have therefore extended this conventional model to exploit concepts derived from domain knowledge. A practical approach to exploiting domain knowledge is to use world knowledge such as the Open Directory Project web directory or the Wikipedia encyclopaedia. Through experiments, we show that our model outperforms other conventional collaborative filtering models while comparing the recommendation quality when using different world knowledge.