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Enhancing tag-based collaborative filtering via integrated social networking information

Citation

Naseri, S and Bahrehmand, A and Ding, C and Chi, C, Enhancing tag-based collaborative filtering via integrated social networking information, Proceedings of 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 25-28 August 2013, Niagra Falls, Canada, pp. 760-764. ISBN 978-1-4503-2240-9 (2013) [Conference Extract]


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DOI: doi:10.1145/2492517.2492658

Abstract

Recently, 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.

Item Details

Item Type:Conference Extract
Keywords:social networking information, collaborative filtering, friendship, membership, social tagging, user similarity
Research Division:Information and Computing Sciences
Research Group:Computation Theory and Mathematics
Research Field:Analysis of Algorithms and Complexity
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Information and Computing Sciences
Author:Chi, C (Dr Chi-Hung Chi)
ID Code:116690
Year Published:2013
Deposited By:Computing and Information Systems
Deposited On:2017-05-17
Last Modified:2017-06-08
Downloads:0

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