research-article

Pretraining Representations of Multi-modal Multi-query E-commerce Search

Authors:

Bo ZhengAuthors Info & Claims

KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 3429 - 3437

https://doi.org/10.1145/3534678.3539200

Published: 14 August 2022 Publication History

Abstract

The importance of modeling contextual information within a search session has been widely acknowledged. However, learning representations of multi-query multi-modal (MM) search, in which Mobile Taobao users repeatedly submit textual and visual queries, remains unexplored in literature. Previous work which learns task-specific representations of textual query sessions fails to capture diverse query types and correlations in MM search sessions. This paper presents to represent MM search sessions by heterogeneous graph neural network (HGN). A multi-view contrastive learning framework is proposed to pretrain the HGN, with two views to model different intra-query, inter-query, and inter-modality information diffusion in MM search. Extensive experiments demonstrate that, the pretrained session representation can benefit state-of-the-art baselines on various downstream tasks, such as personalized click prediction, query suggestion, and intent classification.

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References

[1]

Wasi Uddin Ahmad, Kai-Wei Chang, and Hongning Wang. 2019. Context Attentive Document Ranking and Query Suggestion. In SIGIR. ACM, 385--394.

[2]

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. In NIPS, Vol. 33. Curran Associates, Inc., 1877--1901.

[3]

Jia Chen, Jiaxin Mao, Yiqun Liu, Ziyi Ye, Weizhi Ma, Chao Wang, Min Zhang, and Shaoping Ma. 2021. A Hybrid Framework for Session Context Modeling. ACM Trans. Inf. Syst., Vol. 39, 3, Article 30 (may 2021), 35 pages.

Digital Library

[4]

Jia Chen, Jiaxin Mao, Yiqun Liu, Min Zhang, and Shaoping Ma. 2020. A Context-Aware Click Model for Web Search .ACM, 88--96.

[5]

Wanyu Chen, Fei Cai, Honghui Chen, and Maarten de Rijke. 2018. Attention-Based Hierarchical Neural Query Suggestion. In SIGIR. ACM, 1093--1096.

[6]

Qiannan Cheng, Zhaochun Ren, Yujie Lin, Pengjie Ren, Zhumin Chen, Xiangyuan Liu, and Maarten de de Rijke. 2021. Long Short-Term Session Search: Joint Personalized Reranking and Next Query Prediction .ACM, 239--248.

[7]

Arnon Dagan, Ido Guy, and Slava Novgorodov. 2021. An Image is Worth a Thousand Terms? Analysis of Visual E-Commerce Search. In SIGIR . ACM, 102--112.

[8]

Mostafa Dehghani, Sascha Rothe, Enrique Alfonseca, and Pascal Fleury. 2017. Learning to Attend, Copy, and Generate for Session-Based Query Suggestion. In CIKM . ACM, 1747--1756.

[9]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . Association for Computational Linguistics, 4171--4186.

[10]

John Giorgi, Osvald Nitski, Bo Wang, and Gary Bader. 2021. DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations. In NACCL . Association for Computational Linguistics, 879--895.

[11]

Long Guo, Lifeng Hua, Rongfei Jia, Binqiang Zhao, Xiaobo Wang, and Bin Cui. 2019 b. Buying or Browsing?: Predicting Real-Time Purchasing Intent Using Attention-Based Deep Network with Multiple Behavior. In SIGKDD. ACM, 1984--1992.

Digital Library

[12]

Yangyang Guo, Zhiyong Cheng, Liqiang Nie, Yinglong Wang, Jun Ma, and Mohan Kankanhalli. 2019 a. Attentive Long Short-Term Preference Modeling for Personalized Product Search. ACM Trans. Inf. Syst., Vol. 37, 2, Article 19 (jan 2019), 27 pages.

Digital Library

[13]

Yangyang Guo, Zhiyong Cheng, Liqiang Nie, Xin-Shun Xu, and Mohan Kankanhalli. 2018. Multi-Modal Preference Modeling for Product Search. In MM. ACM, 1865--1873.

Digital Library

[14]

Shashank Gupta and Subhadeep Maji. 2020. Predicting Session Length for Product Search on E-Commerce Platform. In SIGIR . ACM, 1713--1716.

[15]

Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollá r, and Ross B. Girshick. 2021. Masked Autoencoders Are Scalable Vision Learners. CoRR, Vol. abs/2111.06377 (2021).

[16]

Sharon Hirsch, Ido Guy, Alexander Nus, Arnon Dagan, and Oren Kurland. 2020. Query Reformulation in E-Commerce Search. In SIGIR. ACM, 1319--1328.

[17]

Yujing Hu, Qing Da, Anxiang Zeng, Yang Yu, and Yinghui Xu. 2018. Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application. In SIGKDD. ACM, 368--377.

[18]

Jyun-Yu Jiang and Wei Wang. 2018. RIN: Reformulation Inference Network for Context-Aware Query Suggestion. In CIKM . ACM, 197--206.

Digital Library

[19]

Evangelos Kanoulas, Ben Carterette, Paul D. Clough, and Mark Sanderson. 2011. Evaluating Multi-Query Sessions. In SIGIR. ACM, 1053--1062.

[20]

Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR (Poster) . OpenReview.net.

[21]

Katrien Laenen, Susana Zoghbi, and Marie-Francine Moens. 2018. Web Search of Fashion Items with Multimodal Querying. In WSDM. ACM, 342--350.

[22]

Agnès Mustar, Sylvain Lamprier, and Benjamin Piwowarski. 2021. On the Study of Transformers for Query Suggestion. ACM Trans. Inf. Syst., Vol. 40, 1, Article 18 (oct 2021), 27 pages.

[23]

Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. 2020. GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training .ACM, 1150--1160.

Digital Library

[24]

Alessandro Sordoni, Yoshua Bengio, Hossein Vahabi, Christina Lioma, Jakob Grue Simonsen, and Jian-Yun Nie. 2015. A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion. In CIKM. ACM, 553--562.

[25]

Ning Su, Jiyin He, Yiqun Liu, Min Zhang, and Shaoping Ma. 2018. User Intent, Behaviour, and Perceived Satisfaction in Product Search. In WSDM . ACM, 547--555.

[26]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In NIPS. 5998--6008.

[27]

Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR (Poster) . OpenReview.net.

[28]

Xiao Wang, Nian Liu, Hui Han, and Chuan Shi. 2021. Self-Supervised Heterogeneous Graph Neural Network with Co-Contrastive Learning .ACM, 1726--1736.

[29]

Bin Wu, Chenyan Xiong, Maosong Sun, and Zhiyuan Liu. 2018b. Query Suggestion with Feedback Memory Network. In WWW. International World Wide Web Conferences Steering Committee, 1563--1571.

[30]

Liang Wu, Diane Hu, Liangjie Hong, and Huan Liu. 2018a. Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce. In SIGIR . ACM, 365--374.

[31]

Zhirong Wu, Yuanjun Xiong, Stella X. Yu, and Dahua Lin. 2018c. Unsupervised Feature Learning via Non-parametric Instance Discrimination. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . 3733--3742.

[32]

Xiaohui Xie, Jiaxin Mao, Yiqun Liu, Maarten de Rijke, Qingyao Ai, Yufei Huang, Min Zhang, and Shaoping Ma. 2019. Improving Web Image Search with Contextual Information. In CIKM. ACM, 1683--1692.

[33]

Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph Contrastive Learning with Augmentations. In NIPS, Vol. 33. Curran Associates, Inc., 5812--5823.

[34]

Oleg Zendel, J. Shane Culpepper, and Falk Scholer. 2021. Is Query Performance Prediction With Multiple Query Variations Harder Than Topic Performance Prediction?. In SIGIR. ACM, 1713--1717.

[35]

Yuan Zhang, Dong Wang, and Yan Zhang. 2019. Neural IR Meets Graph Embedding: A Ranking Model for Product Search. In WWW . ACM, 2390--2400.

[36]

Jiashu Zhao, Hongshen Chen, and Dawei Yin. 2019. A Dynamic Product-Aware Learning Model for E-Commerce Query Intent Understanding. In CIKM . ACM, 1843--1852.

[37]

Jianling Zhong, Weiwei Guo, Huiji Gao, and Bo Long. 2020. Personalized Query Suggestions .ACM, 1645--1648.

[38]

Yutao Zhu, Jian-Yun Nie, Zhicheng Dou, Zhengyi Ma, Xinyu Zhang, Pan Du, Xiaochen Zuo, and Hao Jiang. 2021. Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking .ACM, New York, NY, USA, 2780--2791.

Cited By

Wen YChen STian YGuan WWang PDeng HXu JZheng BLi ZZou LLi CAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Unified Visual Preference Learning for User Intent UnderstandingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635858(816-825)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635858
Xie JLiu SCong GChen ZChua TNgo CKa-Wei Lee RKumar RLauw H(2024)UnifiedSSR: A Unified Framework of Sequential Search and RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645427(3410-3419)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645427
Palumbo EDamianou AWang ALiu AFazelnia GFabbri FFerreira RSilvestri FBouchard HHauff CLalmas MCarterette BChandar PNyhan DFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Graph Learning for Exploratory Query Suggestions in an Instant Search SystemProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615481(4780-4786)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615481
Show More Cited By

Index Terms

Pretraining Representations of Multi-modal Multi-query E-commerce Search
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2022

5033 pages

ISBN:9781450393850

DOI:10.1145/3534678

General Chairs:
Aidong Zhang
University of Virginia
,
Huzefa Rangwala
Amazon/George Mason University

Copyright © 2022 ACM.

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Published: 14 August 2022

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Natural Science Foundation of China
Alibaba Group through Alibaba Innovative Research program

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KDD '22

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KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 14 - 18, 2022

Washington DC, USA

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Cited By

Wen YChen STian YGuan WWang PDeng HXu JZheng BLi ZZou LLi CAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Unified Visual Preference Learning for User Intent UnderstandingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635858(816-825)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635858
Xie JLiu SCong GChen ZChua TNgo CKa-Wei Lee RKumar RLauw H(2024)UnifiedSSR: A Unified Framework of Sequential Search and RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645427(3410-3419)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645427
Palumbo EDamianou AWang ALiu AFazelnia GFabbri FFerreira RSilvestri FBouchard HHauff CLalmas MCarterette BChandar PNyhan DFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Graph Learning for Exploratory Query Suggestions in an Instant Search SystemProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615481(4780-4786)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615481
Xv GLin CGuan WGou JLi XDeng HXu JZheng BSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)E-commerce Search via Content Collaborative Graph Neural NetworkProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599320(2885-2897)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599320

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