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Cross-domain Attention Network with Wasserstein Regularizers for E-commerce Search

Published: 03 November 2019 Publication History

Abstract

Product search and recommendation is a task that every e-commerce platform wants to outperform their peels on. However, training a good search or recommendation model often requires more data than what many platforms have. Fortunately, the search tasks on different platforms share the common underlying structure. Considering each platform as a domain, we propose a cross-domain learning approach to help the task on data-deficient platforms by leveraging the data from data-abundant platforms. In our solution, the importance of features in different domains is addressed by a domain-specific attention network. Meanwhile, a multi-task regularizer based on Wasserstein distance is introduced to help extract both domain-invariant and domain-specific features. Our model consistently outperforms the competing methods on both public and real-world industry datasets. Quantitative evaluation shows that our model can discover important features for different domains, which helps us better understand different user needs across platforms. Last but not least, we have deployed our model online in three big e-commerce platforms namely Taobao, Tmall, and Qintao, and observed better performance than the production models for all the platforms.

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Cited By

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  • (2022)Cross-Domain Product Search with Knowledge GraphProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557116(3746-3755)Online publication date: 17-Oct-2022
  • (2022)A Centralized-Distributed Transfer Model for Cross-Domain Recommendation Based on Multi-Source Heterogeneous Transfer Learning2022 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM54844.2022.00166(1269-1274)Online publication date: Nov-2022
  • (2022)A Review of Sparse Code Multiple Access Based on Deep LearningAdvances in Natural Computation, Fuzzy Systems and Knowledge Discovery10.1007/978-3-030-89698-0_117(1144-1151)Online publication date: 4-Jan-2022

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cover image ACM Conferences
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management
November 2019
3373 pages
ISBN:9781450369763
DOI:10.1145/3357384
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 03 November 2019

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Author Tags

  1. bo wang
  2. cen chen
  3. deng cai
  4. forrest sheng bao
  5. jingren zhou
  6. jun huang
  7. minghui qiu
  8. xiaoyi zeng

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CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all
  • (2022)Cross-Domain Product Search with Knowledge GraphProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557116(3746-3755)Online publication date: 17-Oct-2022
  • (2022)A Centralized-Distributed Transfer Model for Cross-Domain Recommendation Based on Multi-Source Heterogeneous Transfer Learning2022 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM54844.2022.00166(1269-1274)Online publication date: Nov-2022
  • (2022)A Review of Sparse Code Multiple Access Based on Deep LearningAdvances in Natural Computation, Fuzzy Systems and Knowledge Discovery10.1007/978-3-030-89698-0_117(1144-1151)Online publication date: 4-Jan-2022

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