Optimizing unified loss for web ranking specialization

In this paper, we proposed a novel divide-and-conquer approach to optimize the overall relevance in an unified framework for query clustering and query-based ranking. In our model, latent topics and specialized ranking models are learned iteratively so that an unified objective function, which lower-bounds the conditional probability of observed grades annotated by human editors on training data, is maximized. We conducted experiments comparing the proposed method with several baseline approaches on two data-sets. Experimental results illustrate that our method can significantly improve the ranking relevance over these baselines