Social navigation of food recipes
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Crowdsourcing user studies with Mechanical Turk
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Cross-representation mediation of user models
User Modeling and User-Adapted Interaction
Learning preferences of new users in recommender systems: an information theoretic approach
ACM SIGKDD Explorations Newsletter
Rate it again: increasing recommendation accuracy by user re-rating
Proceedings of the third ACM conference on Recommender systems
Intelligent food planning: personalized recipe recommendation
Proceedings of the 15th international conference on Intelligent user interfaces
Content-based recommendation systems
The adaptive web
On bootstrapping recommender systems
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Deriving a recipe similarity measure for recommending healthful meals
Proceedings of the 16th international conference on Intelligent user interfaces
Tutorial / What every IUI researcher should know about human choice and decision making
Proceedings of the 16th international conference on Intelligent user interfaces
Recipe recommendation: accuracy and reasoning
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Personalized techniques for lifestyle change
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Recommending food: reasoning on recipes and ingredients
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Influencing Individually: Fusing Personalization and Persuasion
ACM Transactions on Interactive Intelligent Systems (TiiS)
Adaptive Persuasive Systems: A Study of Tailored Persuasive Text Messages to Reduce Snacking
ACM Transactions on Interactive Intelligent Systems (TiiS)
Context relevance assessment and exploitation in mobile recommender systems
Personal and Ubiquitous Computing
Evaluating rating scales personality
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
RecSys'12 workshop on human decision making in recommender systems
Proceedings of the sixth ACM conference on Recommender systems
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Personalized systems and recommender systems exploit implicitly and explicitly provided user information to address the needs and requirements of those using their services. User preference information, often in the form of interaction logs and ratings data, is used to identify similar users, whose opinions are leveraged to inform recommendations or to filter information. In this work we explore a different dimension of information trends in user bias and reasoning learned from ratings provided by users to a recommender system. Our work examines the characteristics of a dataset of 100,000 user ratings on a corpus of recipes, which illustrates stable user bias towards certain features of the recipes (cuisine type, key ingredient, and complexity). We exploit this knowledge to design and evaluate a personalized rating acquisition tool based on active learning, which leverages user biases in order to obtain ratings bearing high-value information and to reduce prediction errors with new users.