Adaptive Feedback Methods in an Extended Boolean Model

Relevance feedback methods have been used in information retrieval to generate improved query formulations based on information contained in previously retrieved documents. The relevance feedback techniques have been applied to extended Boolean query formulations as well as to vector query formulations. In this paper, we propose an adaptive way to improve the retrieval performance in an extended Boolean model. We develop a neural network model in which the weights used in extended Boolean queries can be adjusted by users’ relevance feedback. Experiments are performed on a TREC collection and the results show improved performance even after applying the previous feedback methods.