Communications of the ACM
Content-Independent Task-Focused Recommendation
IEEE Internet Computing
Multidimensional Recommender Systems: A Data Warehousing Approach
WELCOM '01 Proceedings of the Second International Workshop on Electronic Commerce
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A graph model for E-commerce recommender systems
Journal of the American Society for Information Science and Technology
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Link prediction approach to collaborative filtering
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Context-Aware SVM for Context-Dependent Information Recommendation
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Supporting Context-Aware Media Recommendations for Smart Phones
IEEE Pervasive Computing
Developing recommender systems with the consideration of product profitability for sellers
Information Sciences: an International Journal
From Web to Social Web: Discovering and Deploying User and Content Profiles
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
Expert Systems with Applications: An International Journal
Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems
Proceedings of the third ACM conference on Recommender systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Comparing context-aware recommender systems in terms of accuracy and diversity
User Modeling and User-Adapted Interaction
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Recently, there has been growing interest in recommender systems (RSs) and particularly in context-aware RSs. Methods for generating context-aware recommendations were classified into the pre-filtering, post-filtering and contextual modeling approaches. This paper focuses on comparing the pre-filtering, the post-filtering, the contextual modeling and the un-contextual approaches and on identifying which method dominates the others and under which circumstances. Although some of these methods have been studied independently, no prior research compared the relative performance to determine which of them is better. This paper proposes an effective method of comparing the three methods to incorporate context and selecting the best alternatives. As a result, it provides analysts with a practical suggestion on how to pick a good approach in an effective manner to improve the performance of a context-aware recommender system.