320 citations · 736 across the 17 of their papers we have counts for
10 papers · 1 filter
Recommendation Fairness: From Static to Dynamic
Dell Zhang, Jun Wang
Driven by the need to capture users' evolving interests and optimize their long-term experiences, more and more recommender systems have started to model recommendation as a Markov…
An Adversarial Imitation Click Model for Information Retrieval
Xinyi Dai, Jianghao Lin, Weinan Zhang +7
Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback. Click models, which study how use…
U-rank: Utility-oriented Learning to Rank with Implicit Feedback
Xinyi Dai, Jiawei Hou, Qing Liu +6
Learning to rank with implicit feedback is one of the most important tasks in many real-world information systems where the objective is some specific utility, e.g., clicks and rev…
A Deep Recurrent Survival Model for Unbiased Ranking
Jiarui Jin, Yuchen Fang, Weinan Zhang +7
Position bias is a critical problem in information retrieval when dealing with implicit yet biased user feedback data. Unbiased ranking methods typically rely on causality models a…
GREASE: A Generative Model for Relevance Search over Knowledge Graphs
Tianshuo Zhou, Ziyang Li, Gong Cheng +2
Relevance search is to find top-ranked entities in a knowledge graph (KG) that are relevant to a query entity. Relevance is ambiguous, particularly over a schema-rich KG like DBped…
Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising
Kan Ren, Yuchen Fang, Weinan Zhang +5
In online advertising, the Internet users may be exposed to a sequence of different ad campaigns, i.e., display ads, search, or referrals from multiple channels, before led up to a…