research-article

Meta-Learning Helps Personalized Product Search

Authors:

Shangsong LiangAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 2277 - 2287

https://doi.org/10.1145/3485447.3512036

Published: 25 April 2022 Publication History

Abstract

Personalized product search that provides users with customized search services is an important task for e-commerce platforms. This task remains a challenge when inferring users’ preferences from few records or even no records, which is also known as the few-shot or zero-shot learning problem. In this paper, we propose a Bayesian Online Meta-Learning Model (BOML), which transfers meta-knowledge, from the inference for other users’ preferences, to help to infer the current user’s interest behind her/his few or even no historical records. To extract meta-knowledge from various inference patterns, our model constructs a mixture of meta-knowledge and transfers the corresponding meta-knowledge to the specific user according to her/his records. Based on the meta-knowledge learned from other similar inferences, our proposed model searches a ranked list of products to meet users’ personalized query intents for those with few search records (i.e., few-shot learning problem) or even no search records (i.e., zero-shot learning problem). Under the records arriving sequentially setting, we propose an online variational inference algorithm to update meta-knowledge over time. Experimental results demonstrate that our proposed BOML outperforms state-of-the-art algorithms.

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Cited By

Mazumder SBanipal IAsthana SZhang B(2024)Label Engineering Methods for ML SystemsIntelligent Systems and Applications10.1007/978-3-031-66336-9_33(464-474)Online publication date: 1-Aug-2024
https://doi.org/10.1007/978-3-031-66336-9_33
Wu BFang JZeng XLiang SZhang QKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Adaptive compositional continual meta-learningProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619963(37358-37378)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619963
Lin ZWang S(2023)Optimization of expression recognition by ResNet18 based on Meta-learningProceedings of the 4th International Conference on Artificial Intelligence and Computer Engineering10.1145/3652628.3652724(580-585)Online publication date: 17-Nov-2023
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Index Terms

Meta-Learning Helps Personalized Product Search
1. Computing methodologies
2. Information systems
  1. Information retrieval
  2. Information systems applications

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

Copyright © 2022 ACM.

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Published: 25 April 2022

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National Natural Science Foundation of China

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WWW '22

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Cited By

Mazumder SBanipal IAsthana SZhang B(2024)Label Engineering Methods for ML SystemsIntelligent Systems and Applications10.1007/978-3-031-66336-9_33(464-474)Online publication date: 1-Aug-2024
https://doi.org/10.1007/978-3-031-66336-9_33
Wu BFang JZeng XLiang SZhang QKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Adaptive compositional continual meta-learningProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619963(37358-37378)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619963
Lin ZWang S(2023)Optimization of expression recognition by ResNet18 based on Meta-learningProceedings of the 4th International Conference on Artificial Intelligence and Computer Engineering10.1145/3652628.3652724(580-585)Online publication date: 17-Nov-2023
https://dl.acm.org/doi/10.1145/3652628.3652724
Wu BMeng ZLiang S(2023)Dynamic Bayesian Contrastive Predictive Coding Model for Personalized Product SearchACM Transactions on the Web10.1145/360922517:4(1-31)Online publication date: 10-Oct-2023
https://dl.acm.org/doi/10.1145/3609225
Wang YChen XFang JMeng ZLiang S(2023)Enhancing Conversational Recommendation Systems with Representation FusionACM Transactions on the Web10.1145/357703417:1(1-34)Online publication date: 21-Feb-2023
https://dl.acm.org/doi/10.1145/3577034

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