A multi source product reputation model |
| |
| Affiliation: | 1. DISP Laboratory, University Lumiere Lyon 2, 160 bd de l’Université, 69676 Bron cedex, France;2. Univ. Lille, CNRS, Centrale Lille, UMR 9189 – CRIStAL (SMAC), F-59000 Lille, France;3. Computer Science Department, Capital University of Science & Technology, Islamabad, Pakistan;4. Computer Science Department, Abdul Wali Khan University Mardan, Pakistan;1. Humboldt-Universität zu Berlin, Institute of Information Systems, Spandauer Str. 1, 10178 Berlin, Germany;2. Hochschule für Telekommunikation Leipzig (HfTL), Chair of Business Intelligence and Data Science, Gustav-Freytag-Str. 43-45, 04277 Leipzig, Germany;3. University of Potsdam, Chair of Business Informatics, esp. Social Media and Data Science, August-Bebel-Str. 89, 14482 Potsdam, Germany;1. Seattle Children''s Research Institute, Seattle, Washington;2. Department of Pediatrics, University of Washington, Seattle, Washington;3. Department of Sociology, University of Washington, Seattle, Washington;4. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington;1. Laboratoire Génie de Production/ENIT-INPT – University of Toulouse, 47 Avenue d’Azereix, 65016 cedex, Tarbes, France;2. Oxford Institute for Sustainable Development, Department of Real Estate and Construction, Faculty of Technology, Design and Environment, Oxford Brookes University, Oxford, OX3 0BP, UK;1. Advanced Laparoscopy and Pelvic Pain, Department of Obstetrics and Gynecology, University of North Carolina, Chapel Hill, NC;2. Maternal and Fetal Medicine, Department of Obstetrics and Gynecology, University of North Carolina, Chapel Hill, NC;1. College of Hospitality and Tourism Management, Sejong University, 98 Gunja-Dong, Gwanjin-Gu, Seoul, 143-747, South Korea;2. Department of Hotel and Restaurant Management, Tongwon University, 26 Gyeongchung-daero, Gonjiam-eup, Gwangju, 12813, South Korea;3. Department of Tourism Management, Dong-A University, 1 Bumin-dong (2 Ga), Seo-gu, Busan, 49236, South Korea |
| |
| Abstract: | Product reputation model is very important for customers and manufacturers in order to make decisions. Several product reputation models are proposed in literature which use customer reviews in order to compute reputation values. However, the aggregation methods used are not able to estimate a good reputation value when some ratings are false. Some of these aggregation methods are not robust to false and biased ratings because a single false rating is enough to change the result. Others are robust to false ratings but not able to reflect the recent opinions about product quickly. In addition, most of the product reputation models are based on single source, therefore suffer from availability and vulnerability issues. In this paper, we propose a multi-source product reputation model where robust and strategy proof aggregation methods are used. A source credibility measure method is proposed, which uses four factors to determine malicious sources. Furthermore, a suitable decay principle for product reputation is also introduced in order to reflect the newest opinions quickly. The results show that proposed model is robust, strategy proof and able to provide a good estimation even if some ratings are false. |
| |
| Keywords: | Product reputation model Reputation system Rating aggregation Product evaluation |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
