User based and item based collaborative filtering with temporal dynamics

Collaborative Filtering or recommender systems use a database for new users and new items about theirs preferences. It is very important to make private suggestions to users, keep their interest alive with admirable suggestions. Collaborative Filtering (CF) is a commonly used system to meet this end. However, despite the fact that CF systems are widely used, traditional CF techniques are unable to track the preferences of users over a period of time. For this reason, “temporal dynamics” has become an important notion in recommendation systems. In this study a new method has been employed to provide customized suggestions to users whose tastes may have changed over time. The proposed system is different from the traditional user-based CF technique and item-based CF technique in that it examines the dates users ranked products and uses this data to help determine user preferences. The evaluation process has been performed on Netflix data in order to measure the success of the system and compare the results with traditional user-based CF technique and item-based C technique. The results are encouraging and the quality of the predictions were significantly improved.