Feature-Weighted User Model for Recommender Systems

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Feature-Weighted User Model for Recommender Systems
Recommender systems are gaining widespread acceptance in e-commerce applications to confront the “information overload” problem. Collaborative Filtering (CF) is a successful recommendation technique, which is based on past ratings of users with similar preferences. In contrast, Content-Based filtering (CB) assumes that each user operates independently. As a result, it exploits only information derived from document or item features. Both approaches have been extensively combined to improve the recommendation procedure. Most of these systems are hybrid: they run CF on the results of CB and vice versa. CF exploits information from the users and their ratings. CB exploits information from items and their features. In this paper, we construct a feature-weighted user profile to disclose the duality between users and features. Exploiting the correlation between users and features we reveal the real reasons of their rating behavior. We perform experimental comparison of the proposed met...
Panagiotis Symeonidis, Alexandros Nanopoulos, Yann
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where UM
Authors Panagiotis Symeonidis, Alexandros Nanopoulos, Yannis Manolopoulos
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