tutorial

Deep Learning for Search and Recommender Systems in Practice

Authors:

Sandeep Kumar Jha,

Mingzhou ZhouAuthors Info & Claims

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 3515 - 3516

https://doi.org/10.1145/3394486.3406709

Published: 20 August 2020 Publication History

Abstract

In this talk, we will go over the components of personalized search and recommender systems and demonstrate the applications of various deep learning techniques along the way.

Search and recommender systems are probably the most prevalent ML powered application across the industry. They share most of the components composition and provide a user a ranked list of items, while there is subtle difference that a search system typically acts passively with a clear user intention in terms of queries and a recommender system acts more proactively.

Deep learning has been wildly successful in solving complex tasks such as image recognition, speech recognition, natural language processing and understanding, machine translation, etc. In the area of personalized recommender systems, deep learning has been showing tremendous impact in recent years.

Search and recommender systems can be staged roughly in three phases: 1. User and query understanding, where a query or a user profile are processed so that the systems can use the processed information to 2. retrieve all the related items (high recall) and 3. rank the items by the order of the most relevance to the user's intent (high precision). Each phase has its unique challenges but deep learning has been ubiquitously pushing beyond the limit.

After walking through the talk, we hope the audience would gain some first-hand experience building a personalized search/recommender system using deep learning techniques.

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Cited By

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
Wu DSun BShang M(2023)Hyperparameter Learning for Deep Learning-Based Recommender SystemsIEEE Transactions on Services Computing10.1109/TSC.2023.323462316:4(2699-2712)Online publication date: 1-Jul-2023
https://doi.org/10.1109/TSC.2023.3234623
Chen WLi H(2023)Stealthy Energy Consumption-oriented Attacks on Training Stage in Deep LearningJournal of Signal Processing Systems10.1007/s11265-023-01895-395:12(1425-1437)Online publication date: 11-Oct-2023
https://doi.org/10.1007/s11265-023-01895-3
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Index Terms

Deep Learning for Search and Recommender Systems in Practice
1. Computing methodologies
  1. Artificial intelligence

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Published In

cover image ACM Conferences

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

August 2020

3664 pages

ISBN:9781450379984

DOI:10.1145/3394486

General Chairs:
Rajesh Gupta
UC San Diego, USA
,
Yan Liu
USC, USA
,
Program Chairs:
Mohak Shah
LG Electronics, USA
,
Suju Rajan
Linkedin, USA
,
Publications Chairs:
Jiliang Tang
Michigan State, USA
,
B. Aditya Prakash
Georgia Tech, USA

Copyright © 2020 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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Published: 20 August 2020

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KDD '20

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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

July 6 - 10, 2020

CA, Virtual Event, USA

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

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Cited By

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
Wu DSun BShang M(2023)Hyperparameter Learning for Deep Learning-Based Recommender SystemsIEEE Transactions on Services Computing10.1109/TSC.2023.323462316:4(2699-2712)Online publication date: 1-Jul-2023
https://doi.org/10.1109/TSC.2023.3234623
Chen WLi H(2023)Stealthy Energy Consumption-oriented Attacks on Training Stage in Deep LearningJournal of Signal Processing Systems10.1007/s11265-023-01895-395:12(1425-1437)Online publication date: 11-Oct-2023
https://doi.org/10.1007/s11265-023-01895-3
Žliobaitė I(2022)Recommender systems for fossil community distribution modellingMethods in Ecology and Evolution10.1111/2041-210X.1391613:8(1690-1706)Online publication date: 23-Jun-2022
https://doi.org/10.1111/2041-210X.13916
Rahim ADurrani MGillani SAli ZHasan NKim M(2022)An efficient recommender system algorithm using trust dataThe Journal of Supercomputing10.1007/s11227-021-03991-278:3(3184-3204)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s11227-021-03991-2
Liu THe Z(2022)A novel personalized recommendation algorithm by exploiting individual trust and item’s similaritiesApplied Intelligence10.1007/s10489-021-02655-152:6(6007-6021)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s10489-021-02655-1
Sun BWu DShang MHe Y(2022)Toward Auto-Learning Hyperparameters for Deep Learning-Based Recommender SystemsDatabase Systems for Advanced Applications10.1007/978-3-031-00126-0_25(323-331)Online publication date: 11-Apr-2022
https://dl.acm.org/doi/10.1007/978-3-031-00126-0_25
Liu HGao QLi JLiao XXiong HChen GWang WYang GZha ZDong DDou DXiong HZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)JIZHI: A Fast and Cost-Effective Model-As-A-Service System for Web-Scale Online Inference at BaiduProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467146(3289-3298)Online publication date: 14-Aug-2021
https://dl.acm.org/doi/10.1145/3447548.3467146

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