A Novel Framework for Ranking Model Adaptation

Domain adaptation is an important problem in learning to rank due to the lack of training data in a new search task. Recently, an approach based on instance weighting and pairwise ranking algorithms has been proposed to address the problem by learning a ranking model for a target domain only using training data from a source domain. In this paper, we propose a novel framework which extends the previous work using a listwise ranking algorithm for ranking adaptation. Our framework firstly estimates the importance weight of a query in the source domain. Then, the importance weight is incorporated into the state-of-the-art listwise ranking algorithm, known as AdaRank. The framework is evaluated on the Letor3.0 benchmark dataset. The results of experiment demonstrate that it can significantly outperform the baseline model which is directly trained on the source domain, and most of the time not significantly worse than the optimal model which is trained on the target domain.