tutorial

Building Recommender Systems with PyTorch

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Vedanuj GoswamiAuthors Info & Claims

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

Pages 3525 - 3526

https://doi.org/10.1145/3394486.3406714

Published: 20 August 2020 Publication History

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Abstract

In this tutorial we show how to build deep learning recommendation systems and resolve the associated interpretability, integrity and privacy challenges. We start with an overview of the PyTorch framework, features that it offers and a brief review of the evolution of recommendation models. We delineate their typical components and build a proxy deep learning recommendation model (DLRM) in PyTorch. Then, we discuss how to interpret recommendation system results as well as how to address the corresponding integrity and quality challenges.

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Mangat BShah SKumbhar VSingh T(2024)Neural Network-Based Hybrid Recommendation System2024 5th International Conference for Emerging Technology (INCET)10.1109/INCET61516.2024.10593547(1-8)Online publication date: 24-May-2024
https://doi.org/10.1109/INCET61516.2024.10593547
Chen XLin DZhang TZhao YLiu HCui YHou CHe JLiang S(2023)Grating waveguides by machine learning for augmented realityApplied Optics10.1364/AO.48628562:11(2924)Online publication date: 6-Apr-2023
https://doi.org/10.1364/AO.486285
Chowdhury DRoy ARamamurthy SRoy N(2023)CHARLIE: A Chatbot That Recommends Daily Fitness and Diet Plans2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops56833.2023.10150359(116-121)Online publication date: 13-Mar-2023
https://doi.org/10.1109/PerComWorkshops56833.2023.10150359
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Building Recommender Systems with PyTorch
1. Computing methodologies

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Published In

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

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

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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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KDD '25

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sigkdd
sigkdd

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

August 3 - 7, 2025

Toronto , ON , Canada

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Mangat BShah SKumbhar VSingh T(2024)Neural Network-Based Hybrid Recommendation System2024 5th International Conference for Emerging Technology (INCET)10.1109/INCET61516.2024.10593547(1-8)Online publication date: 24-May-2024
https://doi.org/10.1109/INCET61516.2024.10593547
Chen XLin DZhang TZhao YLiu HCui YHou CHe JLiang S(2023)Grating waveguides by machine learning for augmented realityApplied Optics10.1364/AO.48628562:11(2924)Online publication date: 6-Apr-2023
https://doi.org/10.1364/AO.486285
Chowdhury DRoy ARamamurthy SRoy N(2023)CHARLIE: A Chatbot That Recommends Daily Fitness and Diet Plans2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops56833.2023.10150359(116-121)Online publication date: 13-Mar-2023
https://doi.org/10.1109/PerComWorkshops56833.2023.10150359
González-Arriaga DVargas-Treviño MVergara-Limon SDe León CLópez-Gómez JVargas-Treviño MGuerrero-García J(2023)Design of a Software Platform to Generate Convolutional Neural Networks for the Parametric Identification of a Cartesian RobotIEEE Access10.1109/ACCESS.2023.328907811(63371-63387)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3289078

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Recommendations

Deep Learning Models for Serendipity Recommendations: A Survey and New Perspectives

A survey of autoencoder-based recommender systems

Building recommender systems for scholarly information

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