June 18, 2024

Sajad Ahmadian

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


An effective trust-based recommendation method using a novel graph clustering algorithm
Type Article
Trust-aware recommender systems; Collaborative filtering; Graph clustering; Sparsest subgraph finding
Researchers Parham Moradi، Sajad Ahmadian، Fardin Akhlaghian


Recommender systems are programs that aim to provide personalized recommendations to users for specific items (e.g. music, books) in online sharing communities or on e-commerce sites. Collaborative filtering methods are important and widely accepted types of recommender systems that generate recommendations based on the ratings of like-minded users. On the other hand, these systems confront several inherent issues such as data sparsity and cold start problems, caused by fewer ratings against the unknowns that need to be predicted. Incorporating trust information into the collaborative filtering systems is an attractive approach to resolve these problems. In this paper, we present a model-based collaborative filtering method by applying a novel graph clustering algorithm and also considering trust statements. In the proposed method first of all, the problem space is represented as a graph and then a sparsest subgraph finding algorithm is applied on the graph to find the initial cluster centers. Then, the proposed graph clustering algorithm is performed to obtain the appropriate users/items clusters. Finally, the identified clusters are used as a set of neighbors to recommend unseen items to the current active user. Experimental results based on three real-world datasets demonstrate that the proposed method outperforms several state-of-the-art recommender system methods.