Adaptive parameter tuning for relevance feedback of information retrieval

Relevance feedback is an effective way to improve the performance of an information retrieval system. In practice, the parameters for feedback were usually determined manually without the consideration of the quality of the query. We propose a new concept (adaptiveness) to measure the quality of the query. We built two models to predict the adaptiveness of the query. The parameters for feedback were then determined by the quality of the query. Our experiments on TREC data showed that the performance was improved significantly when compared with blind relevance feedback.