| Title | Implicit & Explicit Bias in Online Product Reviews used in Recommender Systems |
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Dr. Selwyn Piramuthu |
Abstract
“Recommender systems are gaining popularity as a secondary source of information for customers. These systems use both implicit (transaction data, bookmarks, social networks) and explicit data (customer reviews) to generate recommendations. Implicit data generally have less noise by their very nature and explicit data are notorious for their bias. While published research on various facets of recommender systems is extensive, there are very few published papers that identify or address such bias in explicit data. We consider a few such bias in online product reviews including sequential bias, social bias, among other and discuss their source, dynamic, and implications for recommender systems. ”
Biography
Currently a Professor of Information Systems, Selwyn Piramuthu received his PhD (Information Systems) from the University of Illinois at Urbana-Champaign and has been a faculty member at the University of Florida since Fall 1991. He is an Associate Researcher of Information Systems and Technologies at ESCP-Europe (Ecole Superieure de Commerce de Paris) and a member of the RFID European Lab in Paris. He taught in the Operations and Information Management department at the Wharton School of the University of Pennsylvania from 1998 to 2001. He has also held teaching and/or research visiting appointments at a few other institutions including the Aarhus School of Business in Denmark, Copenhagen Business School in Denmark, IRIDIA (Institut de Recherches Interdisciplinaires et de Developpements en Intelligence Artificielle) at the Universite Libre de Bruxelles in Belgium, Interdisciplinary Center for Security, Reliability and Trust (SnT) at the Universite du Luxembourg in Luxembourg, Technische Universitat Munchen in Munich Germany, and the Lehrstuhl fur Informations- und Technologie- Management at Universitat des Saarlandes in Saarbrucken, Germany. His research and teaching interests include artificial intelligence, cryptography, database management, data mining/machine learning, and simulation including their applications in computer integrated manufacturing, e-commerce, financial credit scoring, healthcare, recommender systems, RFID, supply chain management, and work flow management.
