A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
A collaborative filtering approach to mitigate the new user cold start problem
Knowledge-Based Systems
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
A generalized taxonomy of explanations styles for traditional and social recommender systems
Data Mining and Knowledge Discovery
A framework for collaborative filtering recommender systems
Expert Systems with Applications: An International Journal
Electronic Commerce Research and Applications
Incorporating reliability measurements into the predictions of a recommender system
Information Sciences: an International Journal
A personalized trustworthy seller recommendation in an open market
Expert Systems with Applications: An International Journal
Personalization with Dynamic Group Profile
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Trees for explaining recommendations made through collaborative filtering
Information Sciences: an International Journal
Knowledge-Based Systems
Proceedings of the 22nd international conference on World Wide Web
| Hi-index | 0.00 |
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.