Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Applying case-based reasoning: techniques for enterprise systems
Applying case-based reasoning: techniques for enterprise systems
A prediction system for multimedia pre-fetching in Internet
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Refining Conversational Case Libraries
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Pre-sending Documents on the WWW: A Comparative Study
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
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
Cost-aware WWW proxy caching algorithms
USITS'97 Proceedings of the USENIX Symposium on Internet Technologies and Systems on USENIX Symposium on Internet Technologies and Systems
Predicting file system actions from prior events
ATEC '96 Proceedings of the 1996 annual conference on USENIX Annual Technical Conference
Remembering to forget: a competence-preserving case deletion policy for case-based reasoning systems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
HYREC: a hybrid recommendation system for e-commerce
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
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Case-based reasoning aims to use past experience to solve new problems. A strong requirement for its application is that extensive experience base exists that provides statistically significant justification for new applications. Such extensive experience base has been rare, limiting most CBR applications to be confined to small-scale problems involving single or few users, or even toy problems. In this work, we present an application of CBR in the domain of web document prediction and retrieval, whereby a server-side application can decide, with high accuracy and coverage, a user's next request for hypertext documents based on past requests. An application program can then use the prediction knowledge to prefetch or presend web objects to reduce latency and network load. Through this application, we demonstrate the feasibility of CBR application in the web-document retrieval context, exposing the vast possibility of using web-log files that contain document retrieval experiences from millions of users. In this framework, a CBR system is embedded within an overall web-server application. A novelty of the work is that data mining and case-based reasoning are combined in a seamless manner, allowing cases to be mined efficiently. In addition we developed techniques to allow different case bases to be combined in order to yield a overall case base with higher quality than each individual ones. We validate our work through experiments using realistic, large-scale web logs.