tutorial

Deep Natural Language Processing for Search and Recommender Systems

Authors:

Bee-Chung Chen,

Deepak AgarwalAuthors Info & Claims

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 3199 - 3200

https://doi.org/10.1145/3292500.3332290

Published: 25 July 2019 Publication History

Abstract

Search and recommender systems share many fundamental components including language understanding, retrieval and ranking, and language generation. Building powerful search and recommender systems requires processing natural language effectively and efficiently. Recent rapid growth of deep learning technologies has presented both opportunities and challenges in this area. This tutorial offers an overview of deep learning based natural language processing (NLP) for search and recommender systems from an industry perspective. It first introduces deep learning based NLP technologies, including language understanding and language generation. Then it details how those technologies can be applied to common tasks in search and recommender systems, including query and document understanding, retrieval and ranking, and language generation. Applications in LinkedIn production systems are presented. The tutorial concludes with discussion of future trend.

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Cited By

Tokala SEnduri MLakshmi TSharma H(2023)Community-Based Matrix Factorization (CBMF) Approach for Enhancing Quality of RecommendationsEntropy10.3390/e2509136025:9(1360)Online publication date: 20-Sep-2023
https://doi.org/10.3390/e25091360
Yang WChen CSang CHuang S(2023)Reinforced PU-learning with Hybrid Negative Sampling Strategies for RecommendationACM Transactions on Intelligent Systems and Technology10.1145/358256214:3(1-25)Online publication date: 6-Feb-2023
https://dl.acm.org/doi/10.1145/3582562
Chen KChu GLei KShi YDeng M(2022)A Multiview Representation Learning Framework for Large-Scale Urban Road NetworksApplied Sciences10.3390/app1213630112:13(6301)Online publication date: 21-Jun-2022
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Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2019

3305 pages

ISBN:9781450362016

DOI:10.1145/3292500

General Chairs:
Ankur Teredesai
KenSci
,
Vipin Kumar
University of Minnesota
,
Program Chairs:
Ying Li
EV Analysis Corporation
,
Rómer Rosales
LinkedIn
,
Evimaria Terzi
Boston University
,
George Karypis
University of Minnesota

Copyright © 2019 Owner/Author.

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: 25 July 2019

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KDD '19

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KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 4 - 8, 2019

AK, Anchorage, USA

Acceptance Rates

KDD '19 Paper Acceptance Rate 110 of 1,200 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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KDD '25

Sponsor:
sigkdd
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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

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Citations

Cited By

Tokala SEnduri MLakshmi TSharma H(2023)Community-Based Matrix Factorization (CBMF) Approach for Enhancing Quality of RecommendationsEntropy10.3390/e2509136025:9(1360)Online publication date: 20-Sep-2023
https://doi.org/10.3390/e25091360
Yang WChen CSang CHuang S(2023)Reinforced PU-learning with Hybrid Negative Sampling Strategies for RecommendationACM Transactions on Intelligent Systems and Technology10.1145/358256214:3(1-25)Online publication date: 6-Feb-2023
https://dl.acm.org/doi/10.1145/3582562
Chen KChu GLei KShi YDeng M(2022)A Multiview Representation Learning Framework for Large-Scale Urban Road NetworksApplied Sciences10.3390/app1213630112:13(6301)Online publication date: 21-Jun-2022
https://doi.org/10.3390/app12136301
N KV RRasal PJadhav RSaidireddy MKharade K(2022)Artificial Intelligence based Smart Cosmetics Suggestion System based on Skin Condition2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)10.1109/ICACRS55517.2022.10029120(797-801)Online publication date: 13-Dec-2022
https://doi.org/10.1109/ICACRS55517.2022.10029120
Lombo XOyelade OEzugwu A(2022)Crime Detection and Analysis from Social Media Messages Using Machine Learning and Natural Language Processing TechniqueComputational Science and Its Applications – ICCSA 2022 Workshops10.1007/978-3-031-10548-7_37(502-517)Online publication date: 26-Jul-2022
https://doi.org/10.1007/978-3-031-10548-7_37
Fang ZPan LChen LDu YGao Y(2021)MDTPProceedings of the VLDB Endowment10.14778/3457390.345739414:8(1289-1297)Online publication date: 21-Oct-2021
https://dl.acm.org/doi/10.14778/3457390.3457394
Binabdullah KTongtep N(2021)Comparative Study on Natural Language Processing for Tourism Suggestion System2021 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC)10.1109/ITC-CSCC52171.2021.9501422(1-4)Online publication date: 27-Jun-2021
https://doi.org/10.1109/ITC-CSCC52171.2021.9501422
Chen SChoubey BSingh V(2021)A neural network based price sensitive recommender model to predict customer choices based on price effectJournal of Retailing and Consumer Services10.1016/j.jretconser.2021.10257361(102573)Online publication date: Jul-2021
https://doi.org/10.1016/j.jretconser.2021.102573
Chauhan AHasija Y(2021)NLP-Based Tools for Decoding the Language of LifeProceedings of Emerging Trends and Technologies on Intelligent Systems10.1007/978-981-16-3097-2_18(217-233)Online publication date: 2-Oct-2021
https://doi.org/10.1007/978-981-16-3097-2_18
Wang MLee WFu TYu G(2020)On Representation Learning for Road NetworksACM Transactions on Intelligent Systems and Technology10.1145/342434612:1(1-27)Online publication date: 22-Dec-2020
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