Assistant Professor, School Of Computing
- Keping Bi (2020). https://dl.acm.org/doi/abs/10.1145/3397271.3401192. ACM. Vol. SIGIR.
- Liang Pang (2020). Setrank: Learning a permutation-invariant ranking model for information retrieval. ACM. Vol. SIGIR.
- Keping Bi (2019). Conversational product search based on negative feedback. ACM. Vol. CIKM.
- Xiaohui Xie (2019). Improving Web Image Search with Contextual Information. ACM. Vol. CIKM.
- Qingyao Ai (2019). A zero attention model for personalized product search. ACM. Vol. CIKM.
- Ruey-Cheng Chen (2019). Correcting for Recency Bias in Job Recommendation. ACM. Vol. CIKM.
- Qingyao Ai (2019). Explainable product search with a dynamic relation embedding model. ACM. Vol. TOIS.
- Qingyao Ai, Xuanhui Wang, Sebastian Bruch, Nadav Golbandi, Michael Bendersky & Marc Najork (2019). Learning groupwise multivariate scoring functions using deep neural networks. ACM. Vol. ICTIR19.
- Unbiased Learning to Rank Algorithms (ULTRA).
This is an Unbiased Learning To Rank Algorithms (ULTRA) toolbox, which provides a codebase for experiments and research on learning to rank with human annotated or noisy labels. With the unified data processing pipeline, ULTRA supports multiple unbiased learning-to-rank algorithms, online learning-to-rank algorithms, neural learning-to-rank models, as well as different methods to use and simulate noisy labels (e.g., clicks) to train and test different algorithms/ranking models.
Release Date: 05/01/2020.