Providing Justifications in Recommender Systems

Abstract--Recommender systems are gaining widespread acceptance in e-commerce applications to confront the "information overload" problem. Providing justification to a recommendation gives credibility to a recommender system. Some recommender systems (Amazon.com, etc.) try to explain their recommendations, in an effort to regain customer acceptance and trust. However, their explanations are not sufficient, because they are based solely on rating or navigational data, ignoring the content data. Several systems have proposed the combination of content data with rating data to provide more accurate recommendations, but they cannot provide qualitative justifications. In this paper, we propose a novel approach that attains both accurate and justifiable recommendations. We construct a feature profile for the users to reveal their favorite features. Moreover, we group users into biclusters (i.e., groups of users which exhibit highly correlated ratings on groups of items) to exploit ...

• Type: Journal
• Year: 2008
• Where: TSMC
• Authors: Panagiotis Symeonidis, Alexandros Nanopoulos, Yannis Manolopoulos
• Keywords: Content Data, Recommender Systems, Systems, TSMC 2008