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Paul N. Bennett
Researcher at Microsoft
Publications - 187
Citations - 7308
Paul N. Bennett is an academic researcher from Microsoft. The author has contributed to research in topics: Ranking (information retrieval) & Relevance (information retrieval). The author has an hindex of 40, co-authored 181 publications receiving 5690 citations. Previous affiliations of Paul N. Bennett include Motorola & Carnegie Mellon University.
Papers
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Proceedings ArticleDOI
Guidelines for Human-AI Interaction
Saleema Amershi,Daniel S. Weld,Mihaela Vorvoreanu,Adam Fourney,Besmira Nushi,Penny Collisson,Jina Suh,Shamsi T. Iqbal,Paul N. Bennett,Kori Inkpen,Jaime Teevan,Ruth Kikin-Gil,Eric Horvitz +12 more
TL;DR: This work proposes 18 generally applicable design guidelines for human-AI interaction that can serve as a resource to practitioners working on the design of applications and features that harness AI technologies, and to researchers interested in the further development of human- AI interaction design principles.
Posted Content
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
Lee Xiong,Chenyan Xiong,Ye Li,Kwok-Fung Tang,Jialin Liu,Paul N. Bennett,Junaid Ahmed,Arnold Overwijk +7 more
TL;DR: Approximate nearest neighbor Negative Contrastive Estimation (ANCE) is presented, a training mechanism that constructs negatives from an Approximate Nearest Neighbor (ANN) index of the corpus, which is parallelly updated with the learning process to select more realistic negative training instances.
Proceedings ArticleDOI
Pairwise ranking aggregation in a crowdsourced setting
TL;DR: This work proposes a new model to predict a gold-standard ranking that hinges on combining pairwise comparisons via crowdsourcing and formalizes this as an active learning strategy that incorporates an exploration-exploitation tradeoff and implements it using an efficient online Bayesian updating scheme.
Proceedings ArticleDOI
Modeling the impact of short- and long-term behavior on search personalization
Paul N. Bennett,Ryen W. White,Wei Chu,Susan T. Dumais,Peter Bailey,Fedor Vladimirovich Borisyuk,Xiaoyuan Cui +6 more
TL;DR: This first study to assess how short-term (session) behavior and long- term (historic) behavior interact, and how each may be used in isolation or in combination to optimally contribute to gains in relevance through search personalization finds historic behavior provides substantial benefits at the start of a search session.
Book ChapterDOI
Dual Strategy Active Learning
TL;DR: A dynamic approach, called DUAL, where the strategy selection parameters are adaptively updated based on estimated future residual error reduction after each actively sampled point, to outperform static strategies over a large operating range.