Towards a Better Understanding of Context and Context-Awareness
HUC '99 Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing
The Journal of Machine Learning Research
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
MyMap: Generating personalized tourist descriptions
Applied Intelligence
Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Fast context-aware recommendations with factorization machines
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Collaborative Filtering with Aspect-Based Opinion Mining: A Tensor Factorization Approach
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
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The traditional recommendation systems provide a solution to the problem of information overload. They provide users with the information and content which are the most relevant for them. These systems ignore the fact that users interact with systems in a particular context. Context plays an important role in determining users' behavior by providing additional information that can be exploited in building predictive models. Context-aware recommendation systems take this information into account to make predictions in order to improve the performance of the filtering process. Most existing Context-aware systems use the extrinsic context. In this paper, we propose an intrinsic contextual recommendation system that we can apply to the recommendation of contents in general (i.e. book, Url, item, product, movie, song, restaurant, etc.). The context in our approach is extracted from the set of attributes for the object itself. Our system use a contextual pre-filtering technique based on implicit user feedback. To show the performance of the recommendation process, we consider the movie domain as a case study.