Improving relevance feedback in the vector space model

Abstract

Since the use of relevance f&back in information retrieval to impmve precision and recall was first proposed in the Iate-1960's, many different techniques have been used to improve the results obtained from relevance feedback. Siice most information retrieval systems perfbrming relevance feedback use combinations of several techniques, the individual contribution of each technique to the overall improvement is reIatively unknown. We discuss several techniques to improve relevance feedback including calibrating the number of top-ranked documents or feedback terms used for relevance feedback, clustering the top-ranked documents, changing the term weighting formula, and scaling the weight of the feedback terms. The impact of each technique on improving precision and recall is investigated using the Tipster document collection. We compare our work to a commonly accepted approach of using 50 words and 20 phrases for relevance f&back and show a 3 1% improvement in average precision over the commonly accepted approach when IO feedback terms (either words or phrases) are used. In addition, we have identitied a method which shows promise in predicting those queries which benetit Corn reIevance feedback