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From zero-shot learning to cold-start recommendation

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Zi HuangAuthors Info & Claims

AAAI'19/IAAI'19/EAAI'19: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence

Article No.: 514, Pages 4189 - 4196

https://doi.org/10.1609/aaai.v33i01.33014189

Published: 27 January 2019 Publication History

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Abstract

Zero-shot learning (ZSL) and cold-start recommendation (CSR) are two challenging problems in computer vision and recommender system, respectively. In general, they are independently investigated in different communities. This paper, however, reveals that ZSL and CSR are two extensions of the same intension. Both of them, for instance, attempt to predict unseen classes and involve two spaces, one for direct feature representation and the other for supplementary description. Yet there is no existing approach which addresses CSR from the ZSL perspective. This work, for the first time, formulates CSR as a ZSL problem, and a tailor-made ZSL method is proposed to handle CSR. Specifically, we propose a Lowrank Linear Auto-Encoder (LLAE), which challenges three cruxes, i.e., domain shift, spurious correlations and computing efficiency, in this paper. LLAE consists of two parts, a low-rank encoder maps user behavior into user attributes and a symmetric decoder reconstructs user behavior from user attributes. Extensive experiments on both ZSL and CSR tasks verify that the proposed method is a win-win formulation, i.e., not only can CSR be handled by ZSL models with a significant performance improvement compared with several conventional state-of-the-art methods, but the consideration of CSR can benefit ZSL as well.

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Cited By

Li JShi JZhang JLu YLi QYu CZhang S(2024)Quantum Nearest Neighbor Collaborative Filtering Algorithm for Recommendation SystemACM Transactions on Knowledge Discovery from Data10.1145/367498218:8(1-28)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1145/3674982
Su YLu HLi YLiu LBi SChi EChen MBaeza-Yates RBonchi F(2024)Multi-Task Neural Linear Bandit for Exploration in Recommender SystemsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671649(5723-5730)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671649
Zhang JYu QChen YZhou GLiu YSun YLiang CHuzhang GNi YZeng AYu HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)An E-Commerce Dataset Revealing Variations during SalesProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657870(1162-1171)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657870
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From zero-shot learning to cold-start recommendation

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AAAI'19/IAAI'19/EAAI'19: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence

January 2019

10088 pages

ISBN:978-1-57735-809-1

Copyright © 2019 Association for the Advancement of Artificial Intelligence.

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Association for the Advancement of Artificial Intelligence

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Published: 27 January 2019

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Cited By

Li JShi JZhang JLu YLi QYu CZhang S(2024)Quantum Nearest Neighbor Collaborative Filtering Algorithm for Recommendation SystemACM Transactions on Knowledge Discovery from Data10.1145/367498218:8(1-28)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1145/3674982
Su YLu HLi YLiu LBi SChi EChen MBaeza-Yates RBonchi F(2024)Multi-Task Neural Linear Bandit for Exploration in Recommender SystemsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671649(5723-5730)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671649
Zhang JYu QChen YZhou GLiu YSun YLiang CHuzhang GNi YZeng AYu HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)An E-Commerce Dataset Revealing Variations during SalesProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657870(1162-1171)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657870
Lin ZDing HHoang NKveton BDeoras AWang HAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Pre-trained Recommender Systems: A Causal Debiasing PerspectiveProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635779(424-433)Online publication date: 4-Mar-2024
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Rauf MKhalil MWang WWang QGhani MHassan J(2024)BCE4ZSRInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10368661:3Online publication date: 2-Jul-2024
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Li LBi JYang KLuo F(2024)Di-GraphGANInformation Sciences: an International Journal10.1016/j.ins.2024.120911677:COnline publication date: 1-Aug-2024
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Guo ZShiao WZhang SLiu YChawla NShah NZhao TKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Linkless link prediction via relational distillationProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3618890(12012-12033)Online publication date: 23-Jul-2023
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Razgallah HVlachos MAjalloeian ALiu NSchneider JSteinmann A(2023)Using Neural and Graph Neural Recommender Systems to Overcome Choice Overload: Evidence From a Music Education PlatformACM Transactions on Information Systems10.1145/363787342:4(1-26)Online publication date: 20-Dec-2023
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Fu ZNiu XMaher M(2023)Deep Learning Models for Serendipity Recommendations: A Survey and New PerspectivesACM Computing Surveys10.1145/360514556:1(1-26)Online publication date: 20-Jun-2023
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Cao YYang LWang CLiu ZPeng HYou CYu P(2023)Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608806(322-333)Online publication date: 14-Sep-2023
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