An algorithmic framework for performing collaborative filtering
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A Multilinear Singular Value Decomposition
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Hybrid Recommender Systems: Survey and Experiments
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Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Multidimensional Recommender Systems: A Data Warehousing Approach
WELCOM '01 Proceedings of the Second International Workshop on Electronic Commerce
On the Temporal Analysis for Improved Hybrid Recommendations
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Incorporating contextual information in recommender systems using a multidimensional approach
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Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
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Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
Context-Aware SVM for Context-Dependent Information Recommendation
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Recency-based collaborative filtering
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
A time-based approach to effective recommender systems using implicit feedback
Expert Systems with Applications: An International Journal
Unobtrusive Dynamic Modelling of TV Program Preferences in a Household
EUROITV '08 Proceedings of the 6th European conference on Changing Television Environments
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Using Context to Improve Predictive Modeling of Customers in Personalization Applications
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Using contextual information and multidimensional approach for recommendation
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An empirical study on effectiveness of temporal information as implicit ratings
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Controlled experiments on the web: survey and practical guide
Data Mining and Knowledge Discovery
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal collaborative filtering with adaptive neighbourhoods
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
WAINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications Workshops
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
Time-Dependent Models in Collaborative Filtering Based Recommender System
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Novel Item Recommendation by User Profile Partitioning
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Proceedings of the third ACM conference on Recommender systems
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AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
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Proceedings of the Workshop on Context-Aware Movie Recommendation
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Proceedings of the Workshop on Context-Aware Movie Recommendation
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User Modeling and User-Adapted Interaction
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Exploiting temporal context has been proved to be an effective approach to improve recommendation performance, as shown, e.g. in the Netflix Prize competition. Time-aware recommender systems (TARS) are indeed receiving increasing attention. A wide range of approaches dealing with the time dimension in user modeling and recommendation strategies have been proposed. In the literature, however, reported results and conclusions about how to incorporate and exploit time information within the recommendation processes seem to be contradictory in some cases. Aiming to clarify and address existing discrepancies, in this paper we present a comprehensive survey and analysis of the state of the art on TARS. The analysis show that meaningful divergences appear in the evaluation protocols used--metrics and methodologies. We identify a number of key conditions on offline evaluation of TARS, and based on these conditions, we provide a comprehensive classification of evaluation protocols for TARS. Moreover, we propose a methodological description framework aimed to make the evaluation process fair and reproducible. We also present an empirical study on the impact of different evaluation protocols on measuring relative performances of well-known TARS. The results obtained show that different uses of the above evaluation conditions yield to remarkably distinct performance and relative ranking values of the recommendation approaches. They reveal the need of clearly stating the evaluation conditions used to ensure comparability and reproducibility of reported results. From our analysis and experiments, we finally conclude with methodological issues a robust evaluation of TARS should take into consideration. Furthermore we provide a number of general guidelines to select proper conditions for evaluating particular TARS.