By Topic

Providing Justifications in Recommender Systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Symeonidis, P. ; Dept. of Inf., Aristotle Univ., Thessaloniki ; Nanopoulos, A. ; Manolopoulos, Y.

Recommender systems are gaining widespread acceptance in e-commerce applications to confront the ldquoinformation overloadrdquo 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 partial matching between the preferences of the target user and each group of users. We have evaluated the quality of our justifications with an objective metric in two real data sets (Reuters and MovieLens), showing the superiority of the proposed method over existing approaches.

Published in:

Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on  (Volume:38 ,  Issue: 6 )