Instance cooperative memory to improve query expansion in Information Retrieval Systems
The main goal of this research is to improve Information Retrieval Systems by enabling them to generate search outcomes that are relevant and customized to each specific user. Our proposal advocates the use of Instance Based Reasoning during the information retrieval process. When conducting a search, the system retrieves a previous similar search experience and traces back previous human reasoning and behavior and then replicates it in the current situation. Thus, user information retrieval experiences or instances are saved to be reused in future similar cases. The resulting cooperative memory is used for user query expansion. In order to improve the information retrieval experience, we propose to conceptualize and model both the user profile, and the information retrieval process. This leads us to define some similarity functions between user profiles and information retrieval situations. The reuse of past experiences serves to enrich the initial user query by words from documents found in similar cases. Unlike the classical Rocchio method, these documents are those already judged as valid by users with similar profile and in similar search situation. The value this method brings to the user is an increasing relevance of the search outcomes while reducing user interaction with the system. This method has been implemented in the COSYDOR (Cooperative System for Document Retrieval) prototype based on Intermedia (Oracle 8i). Tests and evaluations have been performed on the COSYDOR prototype using the test corpus of TREC (Text Retrieval Conference) and its standard procedures for performance analysis and benchmarking. The results of these analyses show a significant improvement of performance in the first search iterations compared to the Intermedia benchmark.
cooperative memory query expansion information retrieval relevance feedback user modeling instance based learning