With the abundance of information produced by users on items (e.g., purchase or rating histories), recommender systems are a major ingredients of online systems such as e-stores and e-commerce providers. Recommendation algorithms use information available from users-items interactions and their contextual data to provide a list of potential items for each user. Generally, RS algorithms are constructed based on similarity between users and/or items (e.g., a user is likely to purchase the same items as his/her most similar users). In this paper, we introduce a novel time-aware recommendation algorithm that is based on overlapping community structure between users. Users’ interests might change over time, and thus accurate modelling of dynamic users’ tastes is a challenging issue in designing efficient recommendation systems. The users-items interaction network is often highly sparse in real systems, for which many recommenders fail to provide accurate predictions. We apply the proposed algorithm on a benchmark dataset. Our proposed recommendation algorithm overcomes these challenges and show better precision as compared to the state-of-the-art recommenders.
