7k citations
- University of California, Santa BarbaraUS109 papers
- Microsoft Research (United Kingdom)GB47 papers
- ETH ZurichCH46 papers
- University of California, BerkeleyUS45 papers
- University of Maryland, College ParkUS43 papers
- Carnegie Mellon UniversityUS41 papers
- Stanford UniversityUS39 papers
- University of WashingtonUS36 papers
- Cornell UniversityUS32 papers
- Princeton UniversityUS31 papers
- California Institute of TechnologyUS28 papers
- Georgia Institute of TechnologyUS24 papers
38 papers · 1 filter
Auditing Cross-Cultural Consistency of Human-Annotated Labels for Recommendation Systems
Rock Yuren Pang, Jack Cenatempo, Franklyn Graham +5
Recommendation systems increasingly depend on massive human-labeled datasets; however, the human annotators hired to generate these labels increasingly come from homogeneous backgr…
Unsupervised Dense Retrieval Training with Web Anchors
Yiqing Xie, Xiao Liu, Chenyan Xiong
In this work, we present an unsupervised retrieval method with contrastive learning on web anchors. The anchor text describes the content that is referenced from the linked page. T…
Towards Explainable Collaborative Filtering with Taste Clusters Learning
Yuntao Du, Jianxun Lian, Jing Yao +5
Collaborative Filtering (CF) is a widely used and effective technique for recommender systems. In recent decades, there have been significant advancements in latent embedding-based…
Patterns of gender-specializing query reformulation
Amifa Raj, Bhaskar Mitra, Nick Craswell +1
Users of search systems often reformulate their queries by adding query terms to reflect their evolving information need or to more precisely express their information need when th…
P^3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning
Xiaomeng Hu, Shi Yu, Chenyan Xiong +3
Compared to other language tasks, applying pre-trained language models (PLMs) for search ranking often requires more nuances and training signals. In this paper, we identify and st…
Human Preferences as Dueling Bandits
Xinyi Yan, Chengxi Luo, Charles L. A. Clarke +3
The dramatic improvements in core information retrieval tasks engendered by neural rankers create a need for novel evaluation methods. If every ranker returns highly relevant items…