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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, C
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.

History

Publication title

Proceedings of 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Editors

TO Zyer, P Carrington, EP Lim

Pagination

760-764

ISBN

978-1-4503-2240-9

Department/School

School of Information and Communication Technology

Publisher

Association for Computing Machinery

Place of publication

New York, United States

Event title

2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Event Venue

Niagra Falls, Canada

Date of Event (Start Date)

2013-08-25

Date of Event (End Date)

2013-08-28

Repository Status

  • Restricted

Socio-economic Objectives

Expanding knowledge in the information and computing sciences

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    University Of Tasmania

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