Using time of friendship to improve recommendation

Recommender systems are useful techniques for solving the problem of information overload. Collaborative Filtering (CF) is the most successful approach for recommendation. This approach focuses on previous indicate preferences which is known for its traditional problems such as cold-start, sparsity and hacking. For solving the problem of hacking and improving the accuracy, trust-based CF methods have been proposed previously. These methods focused on trust values among the users. Nonetheless, most existing approaches use trust as a factor independent from time which we think that trust value between users is dynamic; hence it change over time. For this reason, we used friendship time and proposed a novel temporal-trust based approach called AgeTrust to measure trust value. To validate the proposed approach, we used Delicious data set and compared our approach with two other traditional trust-based approaches: traditional CF and FriendshipTrust. Result shows that our proposed approach outperforms the traditional approaches.

Keywords:

component , Recommender systems , collaborative filtering , trust , temporal-trust , social networks