Language Modeling Based Local Set Reranking using Manual Relevance Feedback

We present a novel approach to re-ranking documents using language modeling (LM) and manual relevance feedback (RF). The documents returned by an initial search algorithm, called the Local Set, is reranked based on manual relevance feedback using a ranking function modified to perform at the local set level. Instead of using the query independent collection model, which is too general, we use the query-specific local set, to model the background distribution. The resultant relevance model learns a more specific set of terms relevant to the query. We achieve better ranking performance than existing approaches that employ both LM and RF. We are guided by efficiency considerations and the need of new search paradigms like personalization, that require re-ranking of initial search results based on various criteria rather than launching a fresh search into the entire corpus.