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DAPA: The WSDM 2019 Workshop on Deep Matching in Practical Applications

Published: 30 January 2019 Publication History

Abstract

Matching between two information objects is the core of many different information retrieval (IR) applications including Web search, question answering, and recommendation. Recently, deep learning methods have yielded immense success in speech recognition, computer vision, and natural language processing, significantly advancing state-of-the-art of these areas. In the IR community, deep learning has also attracted much attention, and researchers have proposed a large number of deep matching models to tackle the matching problem for different IR applications. Despite the fact that deep matching models have gained significant progress in these areas, there are still many challenges to be addressed when applying these models to real IR scenarios. In this workshop, we focus on the applicability of deep matching models to practical applications. We aim to discuss the issues of applying deep matching models to production systems, as well as to shed some light on the fundamental characteristics of different matching tasks in IR. website : https://wsdm2019-dapa.github.io/index.html

References

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Jiafeng Guo, Yixing Fan, Qingyao Ai, and W Bruce Croft. 2016. A deep relevance matching model for ad-hoc retrieval. In CIKM. ACM, 55--64.
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Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. In CIKM. ACM, 2333--2338.
[3]
Bhaskar Mitra and Nick Craswell. 2017. Neural Models for Information Retrieval. arXiv preprint arXiv:1705.01509 (2017).
[4]
Bhaskar Mitra, Fernando Diaz, and Nick Craswell. 2017. Learning to Match using Local and Distributed Representations of Text for Web Search. In WWW International World Wide Web Conferences Steering Committee, 1291--1299.
[5]
Yashvardhan Sharma and Sahil Gupta. 2018. Deep Learning Approaches for Question Answering System. Procedia Computer Science, Vol. 132 (2018), 785--794.
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Shuai Zhang, Lina Yao, and Aixin Sun. 2017. Deep learning based recommender system: A survey and new perspectives. arXiv preprint arXiv:1707.07435 (2017).

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  • (2021)Beyond Probability Ranking Principle: Modeling the Dependencies among DocumentsProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462808(2647-2650)Online publication date: 11-Jul-2021

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  1. DAPA: The WSDM 2019 Workshop on Deep Matching in Practical Applications

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      cover image ACM Conferences
      WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining
      January 2019
      874 pages
      ISBN:9781450359405
      DOI:10.1145/3289600
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Publication History

      Published: 30 January 2019

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      Author Tags

      1. deep learning
      2. information retrieval
      3. matching
      4. practical application

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      WSDM '19 Paper Acceptance Rate 84 of 511 submissions, 16%;
      Overall Acceptance Rate 498 of 2,863 submissions, 17%

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      • (2021)Beyond Probability Ranking Principle: Modeling the Dependencies among DocumentsProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462808(2647-2650)Online publication date: 11-Jul-2021

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