Agents that reduce work and information overload
Communications of the ACM
IBM computer usability satisfaction questionnaires: psychometric evaluation and instructions for use
International Journal of Human-Computer Interaction
Cyberguide: a mobile context-aware tour guide
Wireless Networks - Special issue: mobile computing and networking: selected papers from MobiCom '96
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Explanation in Recommender Systems
Artificial Intelligence Review
Using Location for Personalized POI Recommendations in Mobile Environments
SAINT '06 Proceedings of the International Symposium on Applications on Internet
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Supporting Context-Aware Media Recommendations for Smart Phones
IEEE Pervasive Computing
Mobile Interaction Design
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Using Context to Improve Predictive Modeling of Customers in Personalization Applications
IEEE Transactions on Knowledge and Data Engineering
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Enhancing Mobile Recommender Systems with Activity Inference
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Collaborative prediction and ranking with non-random missing data
Proceedings of the third ACM conference on Recommender systems
Context-based splitting of item ratings in collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Context relevance assessment for recommender systems
Proceedings of the 16th international conference on Intelligent user interfaces
Matrix factorization techniques for context aware recommendation
Proceedings of the fifth ACM conference on Recommender systems
SBNMA '11 Proceedings of the 2011 ACM workshop on Social and behavioural networked media access
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Context-aware music recommender systems: workshop keynote abstract
Proceedings of the 21st international conference companion on World Wide Web
A mobile 3D-GIS hybrid recommender system for tourism
Information Sciences: an International Journal
Detecting, acquiring and exploiting contextual information in personalized services
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Semantic context relevance assessment in urban ubiquitous environments
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
A contextual-bandit algorithm for mobile context-aware recommender system
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Exploration / exploitation trade-off in mobile context-aware recommender systems
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Semantically-enhanced pre-filtering for context-aware recommender systems
Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation
Rating Bias and Preference Acquisition
ACM Transactions on Interactive Intelligent Systems (TiiS)
Local context modeling with semantic pre-filtering
Proceedings of the 7th ACM conference on Recommender systems
Information dissemination framework for context-aware products
Computers and Industrial Engineering
I want to view it my way: interfaces to mobile maps should adapt to the user's orientation skills
Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia
A smart TV system with body-gesture control, tag-based rating and context-aware recommendation
Knowledge-Based Systems
Experimental evaluation of context-dependent collaborative filtering using item splitting
User Modeling and User-Adapted Interaction
Review: Mobile recommender systems in tourism
Journal of Network and Computer Applications
Context-aware and automatic configuration of mobile devices in cloud-enabled ubiquitous computing
Personal and Ubiquitous Computing
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In order to generate relevant recommendations, a context-aware recommender system (CARS) not only makes use of user preferences, but also exploits information about the specific contextual situation in which the recommended item will be consumed. For instance, when recommending a holiday destination, a CARS could take into account whether the trip will happen in summer or winter. It is unclear, however, which contextual factors are important and to which degree they influence user ratings. A large amount of data and complex context-aware predictive models must be exploited to understand these relationships. In this paper, we take a new approach for assessing and modeling the relationship between contextual factors and item ratings. Rather than using the traditional approach to data collection, where recommendations are rated with respect to real situations as participants go about their lives as normal, we simulate contextual situations to more easily capture data regarding how the context influences user ratings. To this end, we have designed a methodology whereby users are asked to judge whether a contextual factor (e.g., season) influences the rating given a certain contextual condition (e.g., season is summer). Based on the analyses of these data, we built a context-aware mobile recommender system that utilizes the contextual factors shown to be important. In a subsequent user evaluation, this system was preferred to a similar variant that did not exploit contextual information.