49 citations · 65 across the 9 of their papers we have counts for
4 papers · 2 filters
Iterative Relevance Feedback for Answer Passage Retrieval with Passage-level Semantic Match
Keping Bi, Qingyao Ai, W. Bruce Croft
Relevance feedback techniques assume that users provide relevance judgments for the top k (usually 10) documents and then re-rank using a new query model based on those judgments.…
Revisiting Iterative Relevance Feedback for Document and Passage Retrieval
Keping Bi, Qingyao Ai, W. Bruce Croft
As more and more search traffic comes from mobile phones, intelligent assistants, and smart-home devices, new challenges (e.g., limited presentation space) and opportunities come u…
Unbiased Learning to Rank with Unbiased Propensity Estimation
Qingyao Ai, Keping Bi, Cheng Luo +2
Learning to rank with biased click data is a well-known challenge. A variety of methods has been explored to debias click data for learning to rank such as click models, result int…
Learning a Deep Listwise Context Model for Ranking Refinement
Qingyao Ai, Keping Bi, Jiafeng Guo +1
Learning to rank has been intensively studied and widely applied in information retrieval. Typically, a global ranking function is learned from a set of labeled data, which can ach…