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Enhancing tag-based collaborative filtering via integrated social networking information
conference contribution
posted on 2023-05-24, 16:01 authored by Naseri, S, Bahrehmand, A, Ding, C, Chi, CRecently, researchers have taken tremendous strides in attempting to synthesize conventional social judgments and automated filtering within recommender systems. In this study, we aim to enhance recommendation efficiency via integrating social networking information with traditional recommendation algorithms. To achieve this objective, we first propose a new user similarity metric that not only considers tagging activities of users, but also incorporates their social relationships, such as friendship and membership, in measuring the closeness of two users. Subsequently, we define a new item prediction method which makes use of both user-to-user similarity and item-to-item similarity. Experimental outcomes on Last.fm show some positive results that attest the efficiency of our proposed approach.
History
Publication title
Proceedings of 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningEditors
TO Zyer, P Carrington, EP LimPagination
760-764ISBN
978-1-4503-2240-9Department/School
School of Information and Communication TechnologyPublisher
Association for Computing MachineryPlace of publication
New York, United StatesEvent title
2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningEvent Venue
Niagra Falls, CanadaDate of Event (Start Date)
2013-08-25Date of Event (End Date)
2013-08-28Repository Status
- Restricted