Modeling a user’s click-through behavior in click logs is a challenging task due to the well-known position bias problem. Recent advances in click models have adopted the examin...
Botao Hu, Yuchen Zhang, Weizhu Chen, Gang Wang, Qi...
We present a new approach for personalizing Web search results to a specific user. Ranking functions for Web search engines are typically trained by machine learning algorithms u...
David Sontag, Kevyn Collins-Thompson, Paul N. Benn...
We propose and evaluate a query expansion mechanism that supports searching and browsing in collections of annotated documents. Based on generative language models, our feedback me...
Test collections are the primary drivers of progress in information retrieval. They provide a yardstick for assessing the effectiveness of ranking functions in an automatic, rapi...
Nima Asadi, Donald Metzler, Tamer Elsayed, Jimmy L...
The paper is concerned with applying learning to rank to document retrieval. Ranking SVM is a typical method of learning to rank. We point out that there are two factors one must ...
Yunbo Cao, Jun Xu, Tie-Yan Liu, Hang Li, Yalou Hua...