An Improved Recommender System Based on Forgetting Mechanism for User Interest- Drifting

Highly effective recommender systems may still face users’ interest drifting. one of the main strategies forhandling interest-drifting is forgetting mechanism. current approaches based on forgetting mechanism have somedrawbacks: (i) drifting times are not considered to be detected in user interest over time. (ii) they are not adaptive tothe evolving nature of user’s interest. until now, there hasn’t been any study to overcome these problems. this paperdiscusses the above drawbacks and presents a novel recommender system, named wmidforg, using web usagemining, web content mining techniques, and forgetting mechanism to address user interest-drift problem. we try todetect evolving and time-variant patterns of users' interest individually, and then dynamically use this information topredict favorite items of the user better over time. the experimental results on eachmovie dataset demonstrate ourmethodology increases recommendations precision 6.80% and 1.42% in comparison with available approaches withand without interest-drifting respectively.