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Detect Overlapping Community With Reliability Measure And Rule Based For Improving Recommender Systems
[Full Text]
AUTHOR(S)
Rzgar Sirwan Raza, Sarkhel H.Taher karim
KEYWORDS
Recommender algorithms, social networks, network science, trust, overlapping community structure, reliability.
ABSTRACT
With the affluence of information produced by users in websites, people have problems to find the best information. Recommender systems are a big ingredients of online systems such as e-stores e-commerce providers. Recommendation methods use information available from users-items interactions and their contextual data to provide a best list of items for users. These methods 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 community detection recommendation algorithm that is based on rules. Community detection create groups of interacting vertices (i.e., nodes) in a network depending on their structural properties .We can extract rules in users-items interaction network .use this rules can help recommendation process. We apply the proposed algorithm on a movelense dataset. Our proposed method show better precision as compared to the state-of-the-art recommenders.
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