Mining navigation history for recommendation
Proceedings of the 5th international conference on Intelligent user interfaces
Mining longest repeating subsequences to predict world wide web surfing
USITS'99 Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems - Volume 2
Using Skipping for Sequence-Based Collaborative Filtering
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
A low-order markov model integrating long-distance histories for collaborative recommender systems
Proceedings of the 14th international conference on Intelligent user interfaces
International Journal of Organizational and Collective Intelligence
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This paper focuses on the utilization of the history of navigation within recommender systems. It aims at designing a collaborative recommender based on Markov models relying on partial matching in order to ensure high accuracy, coverage, robustness, low complexity while being anytime. Indeed, contrary to state of the art, this model does not simply match the context of the active user to the context of other users but partial matching is performed: the history of navigation is divided into several sub-histories on which matching is performed, allowing the matching constraints to be weakened. The resulting model leads to an improvement in terms of accuracy compared to state of the art models.