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Naver Search: Deep Learning Powered Search Portal for Intelligent Information Provision

Published: 07 August 2017 Publication History

Abstract

Naver has been the most popular search engine for over a decade in South Korea. As a search portal, Naver aims to match a user's search intentions to the information from the web pages and databases, and to connect users based on shared interests to provide the best way to find the information. Over the past decade, Naver has been trying to better understand Korean users, queries, and web pages for PC and mobile search. In 2002, Naver introduced Knowledge-IN, which was the forerunner of community Question Answering to find out the need of users and topic experts. Users can ask their specific inquiry to appropriate topic experts in their search results. In addition to PC and mobile, Naver is trying to enable a user to access the relevant information using any other device or interface. In detecting common interest groups and good creators, Naver adds device and interface factors. Not only the contents, but also the delivery media types are important in satisfying users on various devices. Deep learning (DL) based methods have tremendous progress in image and text classification. With DL based methods, not only queries, and text documents, but also images, videos, live-streams, locations, etc. are classified and linked to detect common interest groups, and select and rank good creators and good delivery types in each group. With DL, Naver seeks to provide search results that meet user needs more precisely while learning and improving on the fly. In this talk, I'll cover some efforts and challenges in understanding and satisfying users on various devices.

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  1. Naver Search: Deep Learning Powered Search Portal for Intelligent Information Provision

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    cover image ACM Conferences
    SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
    August 2017
    1476 pages
    ISBN:9781450350228
    DOI:10.1145/3077136
    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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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 August 2017

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    Author Tags

    1. common interest group
    2. deep learning
    3. search portal
    4. user understanding

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    SIGIR '17
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    SIGIR '17 Paper Acceptance Rate 78 of 362 submissions, 22%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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