855 citations · 2.6k across the 59 of their papers we have counts for
8 papers · 1 filter
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…
Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks
Qingyao Ai, Xuanhui Wang, Sebastian Bruch +3
While in a classification or a regression setting a label or a value is assigned to each individual document, in a ranking setting we determine the relevance ordering of the entire…
Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
Qingyao Ai, Vahid Azizi, Xu Chen +1
Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms - especially collaborative filtering (CF)-…
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…
Learning over Knowledge-Base Embeddings for Recommendation
Yongfeng Zhang, Qingyao Ai, Xu Chen +1
State-of-the-art recommendation algorithms -- especially the collaborative filtering (CF) based approaches with shallow or deep models -- usually work with various unstructured inf…
aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model
Liu Yang, Qingyao Ai, Jiafeng Guo +1
As an alternative to question answering methods based on feature engineering, deep learning approaches such as convolutional neural networks (CNNs) and Long Short-Term Memory Model…