short-paper

ELECRec: Training Sequential Recommenders as Discriminators

Authors:

Caiming XiongAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2550 - 2554

https://doi.org/10.1145/3477495.3531894

Published: 07 July 2022 Publication History

Abstract

Sequential recommendation is often considered as a generative task, i.e., training a sequential encoder to generate the next item of a user's interests based on her historical interacted items. Despite their prevalence, these methods usually require training with more meaningful samples to be effective, which otherwise will lead to a poorly trained model. In this work, we propose to train the sequential recommenders as discriminators rather than generators. Instead of predicting the next item, our method trains a discriminator to distinguish if a sampled item is a 'real' target item or not. A generator, as an auxiliary model, is trained jointly with the discriminator to sample plausible alternative next items and will be thrown out after training. The trained discriminator is considered as the final SR model and denoted as \modelname. Experiments conducted on four datasets demonstrate the effectiveness and efficiency of the proposed approach.

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Cited By

Shi CYan SZhang SWang HLin K(2025)Knowledge-Guided Semantically Consistent Contrastive Learning for sequential recommendationNeural Networks10.1016/j.neunet.2025.107191185(107191)Online publication date: May-2025
https://doi.org/10.1016/j.neunet.2025.107191
Dang YYang EGuo GJiang LWang XXu XSun QLiu H(2024)TiCoSeRec: Augmenting Data to Uniform Sequences by Time Intervals for Effective RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.332431236:6(2686-2700)Online publication date: Jun-2024
https://doi.org/10.1109/TKDE.2023.3324312
Zhao MSun AYu JLi XHe DYu RYu M(2024)Reliable Data Augmented Contrastive Learning for Sequential RecommendationIEEE Transactions on Big Data10.1109/TBDATA.2024.345375210:6(694-705)Online publication date: Dec-2024
https://doi.org/10.1109/TBDATA.2024.3453752
Show More Cited By

Index Terms

ELECRec: Training Sequential Recommenders as Discriminators
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Learning to rank
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Information & Contributors

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Published In

cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

Copyright © 2022 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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New York, NY, United States

Publication History

Published: 07 July 2022

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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Shi CYan SZhang SWang HLin K(2025)Knowledge-Guided Semantically Consistent Contrastive Learning for sequential recommendationNeural Networks10.1016/j.neunet.2025.107191185(107191)Online publication date: May-2025
https://doi.org/10.1016/j.neunet.2025.107191
Dang YYang EGuo GJiang LWang XXu XSun QLiu H(2024)TiCoSeRec: Augmenting Data to Uniform Sequences by Time Intervals for Effective RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.332431236:6(2686-2700)Online publication date: Jun-2024
https://doi.org/10.1109/TKDE.2023.3324312
Zhao MSun AYu JLi XHe DYu RYu M(2024)Reliable Data Augmented Contrastive Learning for Sequential RecommendationIEEE Transactions on Big Data10.1109/TBDATA.2024.345375210:6(694-705)Online publication date: Dec-2024
https://doi.org/10.1109/TBDATA.2024.3453752
Liu YWang SLi XSun F(2024)A Meta-adversarial Framework for Cross-Domain Cold-Start RecommendationData Science and Engineering10.1007/s41019-024-00245-y9:2(238-249)Online publication date: 5-Mar-2024
https://doi.org/10.1007/s41019-024-00245-y
Zhou KWang HWen JZhao W(2023)Enhancing Multi-View Smoothness for Sequential Recommendation ModelsACM Transactions on Information Systems10.1145/358249541:4(1-27)Online publication date: 8-Apr-2023
https://dl.acm.org/doi/10.1145/3582495
Zhang WSun FLi PZhang ZWang S(2023)Contrastive Learning with Reverse Extended Enhancement for Sequential Recommendation2023 5th International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)10.1109/MLBDBI60823.2023.10482150(63-69)Online publication date: 15-Dec-2023
https://doi.org/10.1109/MLBDBI60823.2023.10482150
Kolikipogu RDarak VYennapu RReddy SSureddi RKuchipudi R(2023)Agriculture Recommender System for Precision Farming using Machine Learning(ARS)2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)10.1109/ICIMIA60377.2023.10426510(921-927)Online publication date: 21-Dec-2023
https://doi.org/10.1109/ICIMIA60377.2023.10426510
Yoon JJang B(2023)Evolution of Deep Learning-Based Sequential Recommender Systems: From Current Trends to New PerspectivesIEEE Access10.1109/ACCESS.2023.328198111(54265-54279)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3281981
Yang MZhou PLi SZhang YHu JZhang A(2023)Multi-Head multimodal deep interest recommendation networkKnowledge-Based Systems10.1016/j.knosys.2023.110689276:COnline publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110689

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