Multidimensional Recommender Systems: A Data Warehousing Approach
WELCOM '01 Proceedings of the Second International Workshop on Electronic Commerce
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Context-Aware SVM for Context-Dependent Information Recommendation
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Using Context to Improve Predictive Modeling of Customers in Personalization Applications
IEEE Transactions on Knowledge and Data Engineering
Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Proceedings of the fourth ACM conference on Recommender systems
The social camera: a case-study in contextual image recommendation
Proceedings of the 16th international conference on Intelligent user interfaces
Fast context-aware recommendations with factorization machines
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
The effect of context-aware recommendations on customer purchasing behavior and trust
Proceedings of the fifth ACM conference on Recommender systems
Context-aware personal route recognition
DS'11 Proceedings of the 14th international conference on Discovery science
Incorporating context into recommender systems: an empirical comparison of context-based approaches
Electronic Commerce Research
SNOPS: a smart environment for cultural heritage applications
Proceedings of the twelfth international workshop on Web information and data management
SmarterDeals: a context-aware deal recommendation system based on the smartercontext engine
CASCON '12 Proceedings of the 2012 Conference of the Center for Advanced Studies on Collaborative Research
Fast ALS-Based tensor factorization for context-aware recommendation from implicit feedback
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Combining user preferences and user opinions for accurate recommendation
Electronic Commerce Research and Applications
Semantically-enhanced pre-filtering for context-aware recommender systems
Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation
Context-aware item-to-item recommendation within the factorization framework
Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation
Context ontologies for recommending from the social web
Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation
Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems
Information Processing and Management: an International Journal
Mining large streams of user data for personalized recommendations
ACM SIGKDD Explorations Newsletter
Cost-Aware Collaborative Filtering for Travel Tour Recommendations
ACM Transactions on Information Systems (TOIS)
Experimental evaluation of context-dependent collaborative filtering using item splitting
User Modeling and User-Adapted Interaction
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
User Modeling and User-Adapted Interaction
Hybreed: A software framework for developing context-aware hybrid recommender systems
User Modeling and User-Adapted Interaction
Comparing context-aware recommender systems in terms of accuracy and diversity
User Modeling and User-Adapted Interaction
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Recently, methods for generating context-aware recommendations were classified into the pre-filtering, post-filtering and contextual modeling approaches. Although some of these methods have been studied independently, no prior research compared the performance of these methods to determine which of them is better than the others. This paper focuses on comparing the pre-filtering and the post-filtering approaches and identifying which method dominates the other and under which circumstances. Since there are no clear winners in this comparison, we propose an alternative more effective method of selecting the winners in the pre- vs. the post-filtering comparison. This strategy provides analysts and companies with a practical suggestion on how to pick a good pre- or post-filtering approach in an effective manner to improve performance of a context-aware recommender system.