b'Qingyao Ai'

Maximizing Marginal Fairness for Dynamic Learning to Rank

Rankings, especially those in search and recommendation systems, often d...
02/18/2021 ∙ by Tao Yang, et al. ∙ 0 ∙ share

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A Zero Attentive Relevance Matching Networkfor Review Modeling in Recommendation System

User and item reviews are valuable for the construction of recommender s...
01/16/2021 ∙ by Hansi Zeng, et al. ∙ 0 ∙ share

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Controlling the Risk of Conversational Search via Reinforcement Learning

Users often formulate their search queries with immature language withou...
01/15/2021 ∙ by Zhenduo Wang, et al. ∙ 0 ∙ share

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A Hierarchical Self-attentive Convolution Network for Review Modeling in Recommendation Systems

Using reviews to learn user and item representations is important for re...
11/26/2020 ∙ by Hansi Zeng, et al. ∙ 0 ∙ share

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Review Regularized Neural Collaborative Filtering

In recent years, text-aware collaborative filtering methods have been pr...
08/20/2020 ∙ by Zhimeng Pan, et al. ∙ 0 ∙ share

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Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to Rank

Leveraging biased click data for optimizing learning to rank systems has...
08/20/2020 ∙ by Tao Yang, et al. ∙ 0 ∙ share

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E-commerce Recommendation with Weighted Expected Utility

Different from shopping at retail stores, consumers on e-commerce platfo...
08/19/2020 ∙ by Zhichao Xu, et al. ∙ 0 ∙ share

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A Transformer-based Embedding Model for Personalized Product Search

Product search is an important way for people to browse and purchase ite...
05/18/2020 ∙ by Keping Bi, et al. ∙ 0 ∙ share

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Unbiased Learning to Rank: Online or Offline?

How to obtain an unbiased ranking model by learning to rank with biased ...
04/28/2020 ∙ by Qingyao Ai, et al. ∙ 0 ∙ share

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A Review-based Transformer Model for Personalized Product Search

In product search, customers make purchase decisions based on not only t...
04/20/2020 ∙ by Keping Bi, et al. ∙ 0 ∙ share

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SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval

In learning-to-rank for information retrieval, a ranking model is automa...
12/12/2019 ∙ by Liang Pang, et al. ∙ 0 ∙ share

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Explainable Product Search with a Dynamic Relation Embedding Model

Product search is one of the most popular methods for customers to disco...
09/16/2019 ∙ by Qingyao Ai, et al. ∙ 0 ∙ share

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Conversational Product Search Based on Negative Feedback

Intelligent assistants change the way people interact with computers and...
09/04/2019 ∙ by Keping Bi, et al. ∙ 0 ∙ share

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A Zero Attention Model for Personalized Product Search

Product search is one of the most popular methods for people to discover...
08/29/2019 ∙ by Qingyao Ai, et al. ∙ 0 ∙ share

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A Deep Look into Neural Ranking Models for Information Retrieval

Ranking models lie at the heart of research on information retrieval (IR...
03/16/2019 ∙ by Jiafeng Guo, et al. ∙ 0 ∙ share

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Iterative Relevance Feedback for Answer Passage Retrieval with Passage-level Semantic Match

Relevance feedback techniques assume that users provide relevance judgme...
12/20/2018 ∙ by Keping Bi, et al. ∙ 0 ∙ share

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Revisiting Iterative Relevance Feedback for Document and Passage Retrieval

As more and more search traffic comes from mobile phones, intelligent as...
12/13/2018 ∙ by Keping Bi, et al. ∙ 0 ∙ share

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Learning Groupwise Scoring Functions Using Deep Neural Networks

While in a classification or a regression setting a label or a value is ...
11/11/2018 ∙ by Qingyao Ai, et al. ∙ 0 ∙ share

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Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

Providing model-generated explanations in recommender systems is importa...
05/09/2018 ∙ by Qingyao Ai, et al. ∙ 0 ∙ share

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Unbiased Learning to Rank with Unbiased Propensity Estimation

Learning to rank with biased click data is a well-known challenge. A var...
04/16/2018 ∙ by Qingyao Ai, et al. ∙ 0 ∙ share

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Learning a Deep Listwise Context Model for Ranking Refinement

Learning to rank has been intensively studied and widely applied in info...
04/16/2018 ∙ by Qingyao Ai, et al. ∙ 0 ∙ share

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Learning over Knowledge-Base Embeddings for Recommendation

State-of-the-art recommendation algorithms -- especially the collaborati...
03/17/2018 ∙ by Yongfeng Zhang, et al. ∙ 0 ∙ share

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aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model

As an alternative to question answering methods based on feature enginee...
01/05/2018 ∙ by Liu Yang, et al. ∙ 0 ∙ share

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A Deep Relevance Matching Model for Ad-hoc Retrieval

In recent years, deep neural networks have led to exciting breakthroughs...
11/23/2017 ∙ by Jiafeng Guo, et al. ∙ 0 ∙ share

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Adaptability of Neural Networks on Varying Granularity IR Tasks

Recent work in Information Retrieval (IR) using Deep Learning models has...
06/24/2016 ∙ by Daniel Cohen, et al. ∙ 0 ∙ share

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Qingyao Ai

Featured Co-authors

Xu Chen

Jun Xu

Xueqi Cheng

Liu Yang

Yanyan Lan

Jiafeng Guo

W. Bruce Croft

Tao Yang

Yongfeng Zhang

Chen Wu

Hamed Zamani

Maximizing Marginal Fairness for Dynamic Learning to Rank

A Zero Attentive Relevance Matching Networkfor Review Modeling in Recommendation System

Controlling the Risk of Conversational Search via Reinforcement Learning

A Hierarchical Self-attentive Convolution Network for Review Modeling in Recommendation Systems

Review Regularized Neural Collaborative Filtering

Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to Rank

E-commerce Recommendation with Weighted Expected Utility

A Transformer-based Embedding Model for Personalized Product Search

Unbiased Learning to Rank: Online or Offline?

A Review-based Transformer Model for Personalized Product Search

SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval

Explainable Product Search with a Dynamic Relation Embedding Model

Conversational Product Search Based on Negative Feedback

A Zero Attention Model for Personalized Product Search

A Deep Look into Neural Ranking Models for Information Retrieval

Iterative Relevance Feedback for Answer Passage Retrieval with Passage-level Semantic Match

Revisiting Iterative Relevance Feedback for Document and Passage Retrieval

Learning Groupwise Scoring Functions Using Deep Neural Networks

Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

Unbiased Learning to Rank with Unbiased Propensity Estimation

Learning a Deep Listwise Context Model for Ranking Refinement

Learning over Knowledge-Base Embeddings for Recommendation

aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model

A Deep Relevance Matching Model for Ad-hoc Retrieval

Adaptability of Neural Networks on Varying Granularity IR Tasks