Ranked search results have become the main mechanism by which we find content,
products, places, and people online. Thus their ordering contributes not only to the …

Automatically judging the quality of retrieval functions based on observable user behavior
holds promise for making retrieval evaluation faster, cheaper, and more user centered …

Accurate estimates of examination bias are crucial for unbiased learning-to-rank from
implicit feedback in search engines and recommender systems, since they enable the use of …

Many real-world data mining tasks require the achievement of two distinct goals when
applied to unseen data: first, to induce an accurate preference ranking, and second to give …

Recent advances in click model have positioned it as an attractive method for representing
user preferences in web search and online advertising. Yet, most of the existing works focus …

Learning to rank from relevance judgment is an active research area. Itemwise score
regression, pairwise preference satisfaction, and listwise structured learning are the major …

This work presents the use of click graphs in improving query intent classifiers, which are
critical if vertical search and general-purpose search services are to be offered in a unified …

Click data collected by modern recommendation systems are an important source of
observational data that can be utilized to train learning-to-rank (LTR) systems. However …

We show that incorporating user behavior data can significantly improve ordering of top
results in real web search setting. We examine alternatives for incorporating feedback into …

LETOR is a benchmark collection for the research on learning to rank for information
retrieval, released by Microsoft Research Asia. In this paper, we describe the details of the …