C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Understanding and Using Context
Personal and Ubiquitous Computing
A Review and Analysis of Commercial User Modeling Servers for Personalization on the World Wide Web
User Modeling and User-Adapted Interaction
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
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Cross-Domain Mediation in Collaborative Filtering
UM '07 Proceedings of the 11th international conference on User Modeling
From Web to Social Web: Discovering and Deploying User and Content Profiles
Context-based splitting of item ratings in collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems
Proceedings of the third ACM conference on Recommender systems
The adaptive web
Proceedings of the fourth ACM conference on Recommender systems
Context-awareness in recommender systems: research workshop and movie recommendation challenge
Proceedings of the fourth ACM conference on Recommender systems
Understanding context before using it
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
Context relevance assessment and exploitation in mobile recommender systems
Personal and Ubiquitous Computing
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
Preface to the special issue on context-aware recommender systems
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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Collaborative Filtering (CF) computes recommendations by leveraging a historical data set of users' ratings for items. CF assumes that the users' recorded ratings can help in predicting their future ratings. This has been validated extensively, but in some domains the user's ratings can be influenced by contextual conditions, such as the time, or the goal of the item consumption. This type of contextual information is not exploited by standard CF models. This paper introduces and analyzes a novel technique for context-aware CF called Item Splitting. In this approach items experienced in two alternative contextual conditions are "split" into two items. This means that the ratings of a split item, e.g., a place to visit, are assigned (split) to two new fictitious items representing for instance the place in summer and the same place in winter. This split is performed only if there is statistical evidence that under these two contextual conditions the items ratings are different; for instance, a place may be rated higher in summer than in winter. These two new fictitious items are then used, together with the unaffected items, in the rating prediction algorithm. When the system must predict the rating for that "split" item in a particular contextual condition (e.g., in summer), it will consider the new fictitious item representing the original one in that particular contextual condition, and will predict its rating. We evaluated this approach on real world, and semi-synthetic data sets using matrix factorization, and nearest neighbor CF algorithms. We show that Item Splitting can be beneficial and its performance depends on the method used to determine which items to split. We also show that the benefit of the method is determined by the relevance of the contextual factors that are used to split.