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SCSS-LIE: A Novel Synchronous Collaborative Search System with a Live Interactive Engine

Published: 18 July 2019 Publication History

Abstract

Synchronous collaborative search systems (SCSS) refer to systems which support two or more users with similar information need to search together simultaneously. Generally, SCSS provide a social engine to enable users to communicate. However, when the number of users in the social engine is insufficient to collaborate on the search task, the social engine will encounter the cold start problem and can not perform collaborative search well. In this paper, we present a novel Synchronous Collaborative Search System with a Live Interactive Engine (SCSS-LIE). SCSS-LIE proposes to apply a ring topology to add an intelligent auxiliary robot, Infobot, into the social engine to support real-time interaction between users and the search engine to address the cold start problem of the social engine. The reading comprehension model BiDAF (Bi-Directional Attention Flow) is employed in the Infobot in the process of interacting with the search engine to obtain answers to facilitate the acquisition of information. SCSS-LIE can not only allow users with similar information need to be grouped into one chat channel to communicate, but also enable them to conduct real-time interaction with the search engine to improve search efficiency.

References

[1]
Zhang Cheng, Zhang Peng, Jingfei Li, and Dawei Song. 2016. SECC:A Novel Search Engine Interface with Live Chat Channel. (2016).
[2]
Colum Foley and Alan F. Smeaton. 2010. Division of labour and sharing of knowledge for synchronous collaborative information retrieval. Information Processing & Management, Vol. 46, 6 (2010), 762--772.
[3]
Wei He, Kai Liu, Jing Liu, Yajuan Lyu, Shiqi Zhao, Xinyan Xiao, Yuan Liu, Yizhong Wang, Hua Wu, and Qiaoqiao She. 2017. DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications. (2017).
[4]
S. Hochreiter and J. Schmidhuber. 1997. Long Short-Term Memory. Neural Computation, Vol. 9, 8 (1997), 1735--1780.
[5]
Hannarin Kruajirayu, Ake Tangsomboon, and Teerapong Leelanupab. 2014. Cozpace: a proposal for collaborative web search for sharing search records and interactions. In Student Project Conference (ICT-ISPC), 2014 Third ICT International. IEEE, 165--168.
[6]
Meredith Ringel Morris. 2007. Collaborating alone and together: Investigating persistent and multi-user web search activities. In Proceedings of International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2007). Amsterdam, Netherland: ACM. 23--27.
[7]
Sindunuraga Rikarno Putra, Felipe Moraes, and Claudia Hauff. 2018. SearchX: Empowering Collaborative Search Research. In International Acm Sigir Conference.
[8]
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016. Bidirectional Attention Flow for Machine Comprehension. (2016).
[9]
Chenguang Zhu, Michael Zeng, and Xuedong Huang. 2018. SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering. (2018).

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  • (2024)Simple but Effective Raw-Data Level Multimodal Fusion for Composed Image RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657727(229-239)Online publication date: 10-Jul-2024

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  1. SCSS-LIE: A Novel Synchronous Collaborative Search System with a Live Interactive Engine

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    cover image ACM Conferences
    SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
    July 2019
    1512 pages
    ISBN:9781450361729
    DOI:10.1145/3331184
    Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 18 July 2019

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    Author Tags

    1. infobot
    2. interactive engine
    3. machine reading comprehension
    4. social engine
    5. synchronous collaborative search systems

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    • Research-article

    Funding Sources

    • Natural Science Foundation of China
    • European Unions Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement
    • State key development program of China

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    SIGIR '19
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    SIGIR'19 Paper Acceptance Rate 84 of 426 submissions, 20%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    • (2024)Simple but Effective Raw-Data Level Multimodal Fusion for Composed Image RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657727(229-239)Online publication date: 10-Jul-2024

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