A New Hybrid Collaborative Recommender Using Semantic Web Technology and Demographic data

Recommender systems are gaining a great importance with the emergence of E-commerce and business on the internet. Collaborative Filtering (CF) is one of the most promising techniques in recommender systems. It uses the known preferences of a group of users to make recommendations for other users. Regardless of its success in many application domains, CF has main limitations such as sparsity, scalability and new user/item problems. As new direction, semantic-based recommenders have emerged that deal with the semantic information of items. Such systems can improve the performance of classical CF by allowing the recommender system to make inferences based on an additional source of knowledge. Moreover, the incorporation of demographic data in recommender systems can help to improve the quality of recommendations. In this paper, we present a new hybrid CF approach that exploits Semantic Web Technology as well as demographic data to alleviate all the problems mentioned above. The experimental results on the MovieLens dataset verify the effectiveness and efficiency of our approach over other benchmarks.

Keywords:

recommender system , collaborative filtering , semantic Web , demographic data , e-commerce