June 18, 2024

Sajad Ahmadian

Academic rank: Assistant professor
Education: Ph.D in Computer Engineering
Phone: 09188339565
Faculty: Faculty of Information Technology


TCARS: Time-and community-aware recommendation system
Type Article
Recommender systems; Social networks; Network science; Overlapping community structure; Reliability
Researchers Fatemeh Rezaeimehr، Parham Moradi، Sajad Ahmadian، Nooruldeen Nasih Qader، Mahdi Jalili


With the abundance of information produced by users on items (e.g., purchase or rating histories), recommender systems are a major ingredient of online systems such as e-stores and service providers. Recommendation algorithms use information available from users–items interactions and their contextual data to provide a list of potential items for each user. These 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 work, we introduce a novel time-aware recommendation algorithm that is based on identifying overlapping community structure among users. Users’ interests might change over time, and accurate modeling of dynamic users’ preferences is a challenging issue in designing efficient personalized recommendation systems. The users–items interaction network is often highly sparse in real systems, for which many recommenders fail to provide accurate predictions. The proposed overlapping community structure amongst the users helps in minimizing the sparsity effects. We apply the proposed algorithm on two real-world benchmark datasets and show that it overcomes these challenges. The proposed algorithm shows better precision than a number of state-of-the-art recommendation methods.