Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Collaborative Filtering with Maximum Entropy
IEEE Intelligent Systems
Improvements in stochastic language modeling
HLT '91 Proceedings of the workshop on Speech and Natural Language
An MDP-Based Recommender System
The Journal of Machine Learning Research
Exploiting headword dependency and predictive clustering for language modeling
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
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
A Maximum Likelihood Approach to Continuous Speech Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A low-order markov model integrating long-distance histories for collaborative recommender systems
Proceedings of the 14th international conference on Intelligent user interfaces
History Dependent Recommender Systems Based on Partial Matching
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
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Recommender systems filter resources for a given user by predicting the most pertinent resource given a specific context. This paper describes a new approach of generating suitable recommendations based on the active user's navigation stream. The underlying hypothesis is that the resources order in the stream results from the intrinsic logic of the user's behavior. The Sequence Based Recommender we propose is inspired from Language Modeling and integrates skipping techniques. It has been tested on a browsing dataset extracted from Intranet logs provided by a French bank. Results show that the use of exponential decay weighting schemes when taking into account non contiguous sequences to compute recommendations enhances the accuracy. Moreover, we propose a skipping variant that provides a high accuracy while being less complex.