Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
The role of transparency in recommender systems
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Making recommendations better: an analytic model for human-recommender interaction
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Bayesian adaptive user profiling with explicit & implicit feedback
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors
Interacting with Computers
Improving new user recommendations with rule-based induction on cold user data
Proceedings of the 2007 ACM conference on Recommender systems
Eigentaste 5.0: constant-time adaptability in a recommender system using item clustering
Proceedings of the 2007 ACM conference on Recommender systems
Addressing cold-start problem in recommendation systems
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Towards personality-based user adaptation: psychologically informed stylistic language generation
User Modeling and User-Adapted Interaction
Enhancing collaborative filtering systems with personality information
Proceedings of the fifth ACM conference on Recommender systems
A study on user perception of personality-based recommender systems
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
The quest for validated personality trait stories
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
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
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Recommender systems help users find personally relevant media content in response to an overwhelming amount of this content available digitally. A prominent issue with recommender systems is recommending new content to new users; commonly referred to as the cold start problem. It has been argued that detailed user characteristics, like personality, could be used to mitigate cold start. To explore this solution, three alternative methods measuring users' personality were compared to investigate which would be most suitable for user information acquisition. Participants (N = 60) provided user ease of use and satisfaction ratings to evaluate three different interface variants believed to measure participants' personality characteristics. Results indicated that the NEO interface and the CFG interface were promising methods for measuring personality. Results are discussed in terms of potential benefits and broader implications for recommender systems.