Mining High-Quality Cases for Hypertext Prediction and Prefetching

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 fro...

• Type: Conference
• Year: 2001
• Where: ICCBR
• Authors: Qiang Yang, Ian Tian Yi Li, Henry Haining Zhang
• Keywords: Automated Reasoning, Case-based Reasoning, Cbr Application, Extensive Experience Base, ICCBR 2001