Democracy is good for ranking: Towards multi-view rank learning and adaptation in web search

Abstract

Web search ranking models are learned from features origi-nated from different views or perspectives of document rel-evancy, such as query dependent or independent features. This seems intuitively conformant to the principle of multi-view approach that leverages distinct complementary views to improve model learning. In this paper, we aim to obtain optimal separation of ranking features into non-overlapping subsets (i.e., views), and use such different views for rank learning and adaptation. We present a novel semi-supervised multi-view ranking model, which is then extended into an adaptive ranker for search domains where no training data exists. The core idea is to proactively strengthen view con-sistency (i.e., the consistency between different rankings each predicted by a distinct view-based ranker) especially when training and test data follow divergent distributions. For this purpose, we propose a unified framework based on list-wise ranking scheme to mutually reinforce the view con-sistency of target queries and the appropriate weighting of source queries that act as prior knowledge. Based on LETOR and Yahoo Learning to Rank datasets, our method signif-icantly outperforms some strong baselines including single-view ranking models commonly used and multi-view ranking models that do not impose view consistency on target data