Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Trust building with explanation interfaces
Proceedings of the 11th international conference on Intelligent user interfaces
A comparative user study on rating vs. personality quiz based preference elicitation methods
Proceedings of the 14th international conference on Intelligent user interfaces
Design and user issues in personality-based recommender systems
Proceedings of the fourth ACM conference on Recommender systems
Comparative evaluation of recommender system quality
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Looking for "good" recommendations: a comparative evaluation of recommender systems
INTERACT'11 Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction - Volume Part III
ACM Transactions on Interactive Intelligent Systems (TiiS)
Evaluating recommender systems from the user's perspective: survey of the state of the art
User Modeling and User-Adapted Interaction
Automatic user preference elicitation for music recommendation
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
How personality influences users' needs for recommendation diversity?
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Using personality to adjust diversity in recommender systems
Proceedings of the 24th ACM Conference on Hypertext and Social Media
Modeling gender differences in healthy eating determinants for persuasive intervention design
PERSUASIVE'13 Proceedings of the 8th international conference on Persuasive Technology
Human Decision Making and Recommender Systems
ACM Transactions on Interactive Intelligent Systems (TiiS)
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To understand users' acceptance of the emerging trend of personality-based recommenders (PBR), we evaluated an existing PBR using the technology acceptance model (TAM). We also compare it with a baseline rating-based recommender in a within-subject user study. Our results show that while the personality-based recommender is perceived to be only slightly more accurate than the rating-based one, it is much easier to use. The side-by-side comparison also reveals that users significantly favor the personality-based recommender and have a significantly higher intention to use such a system again. Therefore, we believe that if users accepted rating-based recommenders, they are most likely to accept personality-based recommenders and personality-based recommenders have a high likelihood to be widely adopted despite the fact that rating-based recommenders are now the industry norm. We further point out some preliminary guidelines on how to design personality-based recommender systems.