Understanding and Using Context
Personal and Ubiquitous Computing
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
Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Proceedings of the fourth ACM conference on Recommender systems
Context relevance assessment for recommender systems
Proceedings of the 16th international conference on Intelligent user interfaces
Flexible recommendation using random walks on implicit feedback graph
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
Fast context-aware recommendations with factorization machines
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Matrix factorization techniques for context aware recommendation
Proceedings of the fifth ACM conference on Recommender systems
Structured collaborative filtering
Proceedings of the 20th ACM international conference on Information and knowledge management
Expert Systems with Applications: An International Journal
Factorization Machines with libFM
ACM Transactions on Intelligent Systems and Technology (TIST)
Context relevance assessment and exploitation in mobile recommender systems
Personal and Ubiquitous Computing
TFMAP: optimizing MAP for top-n context-aware recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Mining contextual movie similarity with matrix factorization for context-aware recommendation
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Exploiting time contexts in collaborative filtering: an item splitting approach
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
Proceedings of the 2013 international conference on Intelligent user interfaces
Which app will you use next?: collaborative filtering with interactional context
Proceedings of the 7th ACM conference on Recommender systems
Query-driven context aware recommendation
Proceedings of the 7th ACM conference on Recommender systems
Local context modeling with semantic pre-filtering
Proceedings of the 7th ACM conference on Recommender systems
Experimental evaluation of context-dependent collaborative filtering using item splitting
User Modeling and User-Adapted Interaction
| Hi-index | 0.00 |
Collaborative Filtering (CF) recommendations are computed by leveraging a historical data set of users' ratings for items. It assumes that the users' previously recorded ratings can help in predicting future ratings. This has been validated extensively, but in some domains item ratings can be influenced by contextual conditions, such as the time or the goal of the item consumption. This type of information is not exploited by standard CF models. This paper introduces and analyzes a novel pre-filtering technique for context-aware CF called item splitting. In this approach, the ratings of certain items are split, according to the value of an item-dependent contextual condition. Each split item generates two fictitious items that are used in the prediction algorithm instead of the original one. 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 item selection method and on the influence of the contextual variables on the item ratings.