Recommender systems based on collaborative filtering and resource allocation

Recommendation systems are important part of electronic commerce, where appropriate items are recommended to potential users. The most common algorithms used for constructing recommender systems in commercial applications are collaborative filtering methods and their variants, which is mainly due to their simple implementation. In these methods, structural features of bipartite network of users and items are used and potential items are recommended to the users based on a similarity measure that shows how similar the behavior of the users is. Indeed, the performance of the memory-based CF algorithms heavily depends on the quality of similarities obtained among users/items. As the obtained similarities are more reliable, better performance for the recommender systems is expected. In this paper, we propose three models to extract reliability of similarities estimated in classic recommenders. We incorporate the obtained reliabilities to improve performance of the recommender systems. In the proposed algorithms for reliability extraction, a number of elements are taken into account including the structure of the user-item bipartite network, the individual profile of the users, i.e., how many items they have rated, and that of the items, i.e., how many users have rated them. Among the proposed methods, the method based on resource allocation provides the highest performance as compared to others. Our numerical results on two benchmark datasets (Movielens and Netflix) shows that employing resource allocation in classical recommenders significantly improves their performance. These results are of great importance since including resource allocation in the systems does not increase their computational complexity.