A SVM based method for active relevance feedback

In vector space models, traditional relevance feedback techniques, which utilize the terms in the relevant documents to enrich the user's initial query, is an effective method to improve retrieval performance. However, in this process, it also brings some non-relevance terms in the relevant documents in the new query. The number of non-relevance terms will increase according to the repeat of feedback process; it will damage the retrieval performance finally. This paper introduces a SVM Based method for relevance feedback. We train a classifier on the feedback documents and classify the rest of the documents. Thus, in the result list, the relevant documents are in front of the non-relevant documents. The new approach avoids modifying the query via text classification algorithm in the relevance feedback process, and it is a new direction for the relevance feedback techniques. Experiments with TREC dataset demonstrate the effectiveness of this method.