Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Trust building with explanation interfaces
Proceedings of the 11th international conference on Intelligent user interfaces
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Critiquing recommenders for public taste products
Proceedings of the third ACM conference on Recommender systems
On the role of diversity in conversational recommender systems
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
A user-centric evaluation framework for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Eye-Tracking study of user behavior in recommender interfaces
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Evaluating recommender systems from the user's perspective: survey of the state of the art
User Modeling and User-Adapted Interaction
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
Correlating perception-oriented aspects in user-centric recommender system evaluation
Proceedings of the 4th Information Interaction in Context Symposium
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Research increasingly indicates that accuracy cannot be the sole criteria in creating a satisfying recommender from the users' point of view. Other criteria, such as diversity, are emerging as important characteristics for consideration as well. In this paper, we try to address the problem of augmenting users' perception of recommendation diversity by applying an organization interface design method to the commonly used list interface. An in-depth user study was conducted to compare an organization interface with a standard list interface. Our results show that the organization interface indeed effectively increased users' perceived diversity of recommendations, especially perceived categorical diversity. Furthermore, 65% of users preferred the organization interface, versus 20% for the list interface. 70% of users thought the organization interface is better at helping them perceive recommendation diversity versus only 15% for the list interface.