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
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
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
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Efficient mining of traversal patterns
Data & Knowledge Engineering - Building web warehouse
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
A Recommendation Algorithm Using Multi-Level Association Rules
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Mining Sequential Association-Rule for Improving WEB Document Prediction
ICCIMA '05 Proceedings of the Sixth International Conference on Computational Intelligence and Multimedia Applications
Mining Sequential Association-Rule for Improving WEB Document Prediction
ICCIMA '05 Proceedings of the Sixth International Conference on Computational Intelligence and Multimedia Applications
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
An Effective Technique for Personalization Recommendation Based on Access Sequential Patterns
APSCC '06 Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing
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
Alambic: a privacy-preserving recommender system for electronic commerce
International Journal of Information Security
Data mining for web personalization
The adaptive web
Content-based recommendation systems
The adaptive web
Knowledge-based navigation of complex information spaces
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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
Association rule mining in peer-to-peer systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Anytime algorithms for top-N recommenders
Proceedings of the 7th ACM conference on Recommender systems
| Hi-index | 0.00 |
Recommendation technologies have traditionally been used in domains such as E-commerce and Web navigation to recommend resources to customers so as to help them to get the pertinent resources. Among the possible approaches is collaborative filtering that does not take into account the content of the resources: only the traces of usage of the resources are considered. State of the art models, such as sequential association-rules and Markov models, that can be used in the frame of privacy concerns, are usually studied in terms of performance, state space complexity and time complexity. Many of them have a large time complexity and require a long time to compute recommendations. However, there are domains of application of the models where recommendations may be required quickly. This paper focuses on the study of how these state of the art models can be adapted so as to be anytime. In that case recommendations can be proposed to the user whatever is the computation time available, the quality of the recommendations increases according to the computation time. We show that such models can be adapted so as to be anytime and we propose several strategies to compute recommendations iteratively. We also show that the computation time needed by these new models is not increased compared to classical ones; even so, it sometimes decreases.