Predicting semantic preferences in a socio-semantic system with collaborative filtering: A case study |
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| Institution: | 1. Room 1103, Bldg 24, ANU College of Business and Economics, Copland Building 24, The Australian National University, ACT, 2601, Australia;2. Dean, College of Business, Zayed University, Abu Dhabi, Khalifa City, FF1-2-051, United Arab Emirates;3. Chair of Marketing and Entrepreneurship, College of Business, Zayed University, Abu Dhabi, Khalifa City, FF1-2-049, United Arab Emirates;1. Department of Industrial Systems and Engineering, Indian Institute of Technology-Kharagpur, Kharagpur, 721302, India;2. Reliability Engineering Centre, Indian Institute of Technology-Kharagpur, Kharagpur 721302, India;1. Fundação Escola de Comércio Álvares Penteado, Campus Liberdade - Avenida da Liberdade, 532 - Liberdade, São Paulo - S.P., 01502-001, Brazil;2. Montpellier Business School, 2300, Avenue Des Moulins, Montpellier – Cédex 4, 34185, France;1. Programa de Pós-Graduação em Informática, Universidade Federal do Rio de Janeiro, Av. Athos da Silveira Ramos, 274 – Bl. E – CCMN/NCE, 21.941-590, Cidade Universitária, Rio de Janeiro, RJ, Brazil;2. Departamento de Ciência da Computação, Universidade Federal do Rio de Janeiro, Av. Athos da Silveira Ramos, 274 – Bl. E – Room 1038 (NCE), 21.941-916, Cidade Universitária, Rio de Janeiro, RJ, Brazil;1. School of Economics and Management, Nanjing University of Science &Technology, 200, Xiaolinwei Road, Nanjing, Jiangsu, 210094, China;2. College of Business, University of Alabama in Huntsville, AL, 35899, United States;3. School of Computing and Information Sciences, University of Pittsburgh, 135 North Bellefield Avenue, Pittsburgh, PA, 15260, United States;1. Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, Largo Lucio Lazzarino, 1, 56122 Pisa, Italy;2. Department of Enterprise Engineering, University of Rome Tor Vergata, Via Orazio Raimondo, 00173, Rome, Italy |
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| Abstract: | This paper proposes collaborative filtering as a means to predict semantic preferences by combining information on social ties with information on links between actors and semantics. First, the authors present an overview of the most relevant collaborative filtering approaches, showing how they work and how they differ. They then compare three different collaborative filtering algorithms using articles published by New York Times journalists from 2003 to 2005 to predict preferences, where preferences refer to journalists’ inclination to use certain words in their writing. Results show that while preference profile similarities in an actor’s neighbourhood are a good predictor of her semantic preferences, information on her social network adds little to prediction accuracy. |
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| Keywords: | Collaborative filtering Socio-semantic system Links prediction Semantic preferences Social networks Semantic networks |
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