855 citations · 2.6k across the 59 of their papers we have counts for
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Model-agnostic vs. Model-intrinsic Interpretability for Explainable Product Search
Qingyao Ai, Lakshmi Narayanan Ramasamy
Product retrieval systems have served as the main entry for customers to discover and purchase products online. With increasing concerns on the transparency and accountability of A…
ULTRA: An Unbiased Learning To Rank Algorithm Toolbox
Anh Tran, Tao Yang, Qingyao Ai
Learning to rank systems has become an important aspect of our daily life. However, the implicit user feedback that is used to train many learning to rank models is usually noisy a…
Asking Clarifying Questions Based on Negative Feedback in Conversational Search
Keping Bi, Qingyao Ai, W. Bruce Croft
Users often need to look through multiple search result pages or reformulate queries when they have complex information-seeking needs. Conversational search systems make it possibl…
Understanding the Effectiveness of Reviews in E-commerce Top-N Recommendation
Zhichao Xu, Hansi Zeng, Qingyao Ai
Modern E-commerce websites contain heterogeneous sources of information, such as numerical ratings, textual reviews and images. These information can be utilized to assist recommen…
A Neural Passage Model for Ad-hoc Document Retrieval
Qingyao Ai, Brendan O Connor, W. Bruce Croft
Traditional statistical retrieval models often treat each document as a whole. In many cases, however, a document is relevant to a query only because a small part of it contain the…
Maximizing Marginal Fairness for Dynamic Learning to Rank
Tao Yang, Qingyao Ai
Rankings, especially those in search and recommendation systems, often determine how people access information and how information is exposed to people. Therefore, how to balance t…
Controlling the Risk of Conversational Search via Reinforcement Learning
Zhenduo Wang, Qingyao Ai
Users often formulate their search queries with immature language without well-developed keywords and complete structures. Such queries fail to express their true information needs…
A Zero Attentive Relevance Matching Networkfor Review Modeling in Recommendation System
Hansi Zeng, Zhichao Xu, Qingyao Ai
User and item reviews are valuable for the construction of recommender systems. In general, existing review-based methods for recommendation can be broadly categorized into two gro…