Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Using speculation to reduce server load and service time on the WWW
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Using predictive prefetching to improve World Wide Web latency
ACM SIGCOMM Computer Communication Review
Mining navigation history for recommendation
Proceedings of the 5th international conference on Intelligent user interfaces
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Selective Markov models for predicting Web page accesses
ACM Transactions on Internet Technology (TOIT)
Collaborative Filtering with Maximum Entropy
IEEE Intelligent Systems
A Web Recommendation System Based on Maximum Entropy
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume I - Volume 01
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
Collaborative Filtering by Mining Association Rules from User Access Sequences
WIRI '05 Proceedings of the International Workshop on Challenges in Web Information Retrieval and Integration
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
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
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
Natural language processing for usage based indexing of web resources
ECIR'07 Proceedings of the 29th European conference on IR research
Mining significant usage patterns from clickstream data
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
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
Towards tabbing aware recommendations
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
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Recommender systems provide users with pertinent resources according to their context and their profiles, by applying statistical and knowledge discovery techniques. This paper describes a new approach of generating suitable recommendations based on the active user's navigation stream, by considering long and short-distance resources in the history with a tractable model. The Skipping Based Recommender we propose uses Markov models inspired from the ones used in language modeling while integrating skipping techniques to handle noise during navigation. Weighting schemes are also used to alleviate the importance of distant resources. This recommender has also the characteristic to be anytime. 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 resources to compute recommendations enhances the accuracy. Moreover, the skipping variant we propose provides a high accuracy while being less complex than state of the art variants.