Opposite re-ranking based on relevant sub-topic dispelling

Opposite re-ranking is a novel strategy to personalized information retrieval, it utilizes the description structure opposed to query intention, to achieve the recognition and dispelling of obstinate-negative feedback. At present, an important problem of the research in the opposite re-ranking is how to establish the maximum discriminative and representative opposite query intention. Aiming at the problem, this paper proposes an opposite re-ranking method based on dispelling of the relevant subtopics for the optimization of result list. The experiment makes use of TDT4 corpora to test, the results present this approach improves the search performance dramatically, the MAP enhances by 16 percents, P@20 and NDCG@20 increases by 14 percents and 12 percents respectively.