A review of temporal recommendation systems

In recommender systems, users’ preferences typically change over time. Modeling and capturing the temporal dynamics of users’ preferences lead to significant improvements in recommendation accuracy and thus users’ satisfaction. Temporal recommendation algorithms exploit time information for modeling the dynamics of user’s preferences over time aiming to improve the quality of recommendations. In this paper, we review and analyze the state-of-the-art methods that use the temporal dynamics to improve recommendation accuracy and identify and discuss important challenges and open problems in the direction of temporal collaborative filtering that can be anticipated to be valuable for future research.

Keywords:

Recommendation System , Temporal Dynamics , Users Preference dynamics.