Multi-Aspect Dense Retrieval-Reference-Cited by-同舟云学术

Multi-Aspect Dense Retrieval

Published:2022-08-14 Issue: Volume: Page:
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Container-title:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Kong Weize¹,Khadanga Swaraj¹,Li Cheng¹,Gupta Shaleen Kumar¹,Zhang Mingyang¹,Xu Wensong¹,Bendersky Michael¹

Affiliation:

1. Google, Mountain View, CA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3534678.3539137

Reference37 articles.

1. Learning a Hierarchical Embedding Model for Personalized Product Search

2. Alexandr Andoni , Piotr Indyk , and Ilya Razenshteyn . Approximate nearest neighbor search in high dimensions . In Proceedings of the International Congress of Mathematicians: Rio de Janeiro 2018 , pages 3287 -- 3318 . World Scientific , 2018 . Alexandr Andoni, Piotr Indyk, and Ilya Razenshteyn. Approximate nearest neighbor search in high dimensions. In Proceedings of the International Congress of Mathematicians: Rio de Janeiro 2018, pages 3287--3318. World Scientific, 2018.

3. Multi-task learning for learning to rank in web search

4. Scalable Attentive Sentence Pair Modeling via Distilled Sentence Embedding

5. Multi-task learning for boosting with application to web search ranking

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