research-article

Learning to Learn a Cold-start Sequential Recommender

Authors:

Changsheng XuAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 40, Issue 2

Article No.: 30, Pages 1 - 25

https://doi.org/10.1145/3466753

Published: 01 February 2022 Publication History

Abstract

The cold-start recommendation is an urgent problem in contemporary online applications. It aims to provide users whose behaviors are literally sparse with as accurate recommendations as possible. Many data-driven algorithms, such as the widely used matrix factorization, underperform because of data sparseness. This work adopts the idea of meta-learning to solve the user’s cold-start recommendation problem. We propose a meta-learning-based cold-start sequential recommendation framework called metaCSR, including three main components: Diffusion Representer for learning better user/item embedding through information diffusion on the interaction graph; Sequential Recommender for capturing temporal dependencies of behavior sequences; and Meta Learner for extracting and propagating transferable knowledge of prior users and learning a good initialization for new users. metaCSR holds the ability to learn the common patterns from regular users’ behaviors and optimize the initialization so that the model can quickly adapt to new users after one or a few gradient updates to achieve optimal performance. The extensive quantitative experiments on three widely used datasets show the remarkable performance of metaCSR in dealing with the user cold-start problem. Meanwhile, a series of qualitative analysis demonstrates that the proposed metaCSR has good generalization.

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Index Terms

Learning to Learn a Cold-start Sequential Recommender
1. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
      1. Personalization

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 40, Issue 2

April 2022

587 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3484931

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Copyright © 2022 Association for Computing Machinery.

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Publication History

Published: 01 February 2022

Accepted: 01 May 2021

Revised: 01 April 2021

Received: 01 November 2020

Published in TOIS Volume 40, Issue 2

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National Key R&D Program of China
Fundamental Research Funds for the Central Universities
Beijing Natural Science Foundation
National Natural Science Foundation of China

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Yin MBoughanmi KAnsari A(2024)Meta-Learning Customer Preference Dynamics on Digital PlatformsSSRN Electronic Journal10.2139/ssrn.4727171Online publication date: 2024
https://doi.org/10.2139/ssrn.4727171
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Esmeli RAbdullahi HBader-El-Den MCan A(2024)Session context data integration to address the cold start problem in e-commerce recommender systemsDecision Support Systems10.1016/j.dss.2024.114339187(114339)Online publication date: Dec-2024
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