GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
What we talk about when we talk about context
Personal and Ubiquitous Computing
Taxonomy-driven computation of product recommendations
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Dynamically-optimized context in recommender systems
Proceedings of the 6th international conference on Mobile data management
Improving Recommendation Novelty Based on Topic Taxonomy
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
From Web to Social Web: Discovering and Deploying User and Content Profiles
Exploiting Item Taxonomy for Solving Cold-Start Problem in Recommendation Making
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Recommender systems are based mainly on collaborative filtering algorithms, which only use the ratings given by the users to the products. When context is taken into account, there might be difficulties when it comes to making recommendations to users who are placed in a context other than the usual one, since their preferences will not correlate with the preferences of those in the new context. In this paper, a hybrid collaborative filtering model is proposed, which provides recommendations based on the context of the travelling users. A combination of a user-based collaborative filtering method and a semantic-based one has been used. Contextual recommendation may be applied in multiple social networks that are spreading world-wide. The resulting system has been tested over 11870.com, a good example of a social network where context is a primary concern.