research-article

MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation

Authors:

Liming ZhuAuthors Info & Claims

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

Pages 688 - 697

https://doi.org/10.1145/3394486.3403113

Published: 20 August 2020 Publication History

Abstract

A common challenge for most current recommender systems is the cold-start problem. Due to the lack of user-item interactions, the fine-tuned recommender systems are unable to handle situations with new users or new items. Recently, some works introduce the meta-optimization idea into the recommendation scenarios, i.e. predicting the user preference by only a few of past interacted items. The core idea is learning a global sharing initialization parameter for all users and then learning the local parameters for each user separately. However, most meta-learning based recommendation approaches adopt model-agnostic meta-learning for parameter initialization, where the global sharing parameter may lead the model into local optima for some users. In this paper, we design two memory matrices that can store task-specific memories and feature-specific memories. Specifically, the feature-specific memories are used to guide the model with personalized parameter initialization, while the task-specific memories are used to guide the model fast predicting the user preference. And we adopt a meta-optimization approach for optimizing the proposed method. We test the model on two widely used recommendation datasets and consider four cold-start situations. The experimental results show the effectiveness of the proposed methods.

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Cited By

Ma RWang LWu HGao BWang XZhao L(2025)Historical Trends and Normalizing Flow for One-shot Temporal Knowledge Graph ReasoningExpert Systems with Applications10.1016/j.eswa.2024.125366260(125366)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2024.125366
Qiu LZhou WTian YUl Haq AAhmad S(2024)Leveraging user’s preference and social circle for personalized recommendation via matrix factorization with sub-linear convergence rateJournal of Intelligent & Fuzzy Systems10.3233/JIFS-23126447:1-2(1-13)Online publication date: 18-Nov-2024
https://doi.org/10.3233/JIFS-231264
GAO YLIU SGUO BXU XBIAN HHAO JXU WYU Z(2024)Lightweight sensing-computing-decision collaboration enhancement for multi-mobile terminalsSCIENTIA SINICA Informationis10.1360/SSI-2024-008954:9(2136)Online publication date: 9-Sep-2024
https://doi.org/10.1360/SSI-2024-0089
Show More Cited By

Index Terms

MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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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

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Published: 20 August 2020

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July 6 - 10, 2020

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Cited By

Ma RWang LWu HGao BWang XZhao L(2025)Historical Trends and Normalizing Flow for One-shot Temporal Knowledge Graph ReasoningExpert Systems with Applications10.1016/j.eswa.2024.125366260(125366)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2024.125366
Qiu LZhou WTian YUl Haq AAhmad S(2024)Leveraging user’s preference and social circle for personalized recommendation via matrix factorization with sub-linear convergence rateJournal of Intelligent & Fuzzy Systems10.3233/JIFS-23126447:1-2(1-13)Online publication date: 18-Nov-2024
https://doi.org/10.3233/JIFS-231264
GAO YLIU SGUO BXU XBIAN HHAO JXU WYU Z(2024)Lightweight sensing-computing-decision collaboration enhancement for multi-mobile terminalsSCIENTIA SINICA Informationis10.1360/SSI-2024-008954:9(2136)Online publication date: 9-Sep-2024
https://doi.org/10.1360/SSI-2024-0089
Wang YGe YLi ZLi LChen R(2024)M3Rec: A Context-Aware Offline Meta-Level Model-Based Reinforcement Learning Approach for Cold-Start RecommendationACM Transactions on Information Systems10.1145/365994742:6(1-27)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3659947
Wang JRathi PSundaram H(2024)A Pre-trained Zero-shot Sequential Recommendation Framework via Popularity DynamicsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688145(433-443)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688145
Balasubramanian KAlshabanah AMarkowitz EVer Steeg GAnnavaram M(2024)Biased User History Synthesis for Personalized Long-Tail Item RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688141(189-199)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688141
Chen GSun RJiang YCao JZhang QLin JLi HGai KZhang X(2024)A Multi-modal Modeling Framework for Cold-start Short-video RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688098(391-400)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688098
Wang YPiao HDong DYao QZhou JBaeza-Yates RBonchi F(2024)Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature InteractionsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671784(3233-3244)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671784
Salganik RLiu XMa YKang JChua TBaeza-Yates RBonchi F(2024)LARP: Language Audio Relational Pre-training for Cold-Start Playlist ContinuationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671772(2524-2535)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671772
Kim JKim EYeo KJeon YKim CLee SLee JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Content-based Graph Reconstruction for Cold-start Item RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657801(1263-1273)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657801
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