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.