Effect of Rating Time for Cold Start Problem in Collaborative Filtering

Cold start is one of the main challenges in recommender systems. Solving sparsechallenge of cold start users is hard. More cold start users and items are new. Sine many general methods for recommender systems has over fittingon cold start users and items, so recommendation to new users and items is important and hard duty. In this work to overcome sparse problem, we present a new method for recommender system based on tensor decomposition that use time dimension as independent dimension. Our method uses extra information of sequence of rating time which specify time duration of ratings. We test our method on dataset of Each Movie with 2 data types. One type has cold start users and items and another hasn’t cold start users and items. Result shows that using time dimension has more effect on cold start users and items than others.