Abstract:
Ranking is the central problem for information retrieval (IR), and employing machine learning techniques to learn the ranking function is viewed as a promising approach to IR. In information retrieval, the users’queries often vary a lot from one to another. However most of existing approaches for ranking do not explicitly take into consideration the fact that queries vary significantly along several dimensions. In this paper, query difference is incorporated into ranking by applying query-dependent loss function to the original loss function of RankBoost algorithm. The effectiveness of the system will be tested on LETOR, publicly available benchmark dataset.
