36 citations · 70 across the 11 of their papers we have counts for
11 papers
Beyond Exposure: Optimizing Ranking Fairness with Non-linear Time-Income Functions
Xuancheng Li, Tao Yang, Yujia Zhou +2
Ranking systems in web search and recommendation allocate attention among items and providers, and therefore need to balance relevance-based effectiveness with provider fairness. E…
Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach
Tao Yang, Cuize Han, Chen Luo +3
Ranking is at the core of many artificial intelligence (AI) applications, including search engines, recommender systems, etc. Modern ranking systems are often constructed with lear…
FARA: Future-aware Ranking Algorithm for Fairness Optimization
Tao Yang, Zhichao Xu, Zhenduo Wang +1
Ranking systems are the key components of modern Information Retrieval (IR) applications, such as search engines and recommender systems. Besides the ranking relevance to users, th…
Marginal-Certainty-aware Fair Ranking Algorithm
Tao Yang, Zhichao Xu, Zhenduo Wang +2
Ranking systems are ubiquitous in modern Internet services, including online marketplaces, social media, and search engines. Traditionally, ranking systems only focus on how to get…
Reinforcement Learning to Rank Using Coarse-grained Rewards
Yiteng Tu, Zhichao Xu, Tao Yang +6
Learning to rank (LTR) plays a crucial role in various Information Retrieval (IR) tasks. Although supervised LTR methods based on fine-grained relevance labels (e.g., document-leve…
Learning to Rank Rationales for Explainable Recommendation
Zhichao Xu, Yi Han, Tao Yang +2
State-of-the-art recommender system (RS) mostly rely on complex deep neural network (DNN) model structure, which makes it difficult to provide explanations along with RS decisions.…
Vertical Allocation-based Fair Exposure Amortizing in Ranking
Tao Yang, Zhichao Xu, Qingyao Ai
Result ranking often affects consumer satisfaction as well as the amount of exposure each item receives in the ranking services. Myopically maximizing customer satisfaction by rank…
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…
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…
Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to Rank
Tao Yang, Shikai Fang, Shibo Li +2
Leveraging biased click data for optimizing learning to rank systems has been a popular approach in information retrieval. Because click data is often noisy and biased, a variety o…
Unbiased Learning to Rank: Online or Offline?
Qingyao Ai, Tao Yang, Huazheng Wang +1
How to obtain an unbiased ranking model by learning to rank with biased user feedback is an important research question for IR. Existing work on unbiased learning to rank (ULTR) ca…