research-article

Unified Visual Preference Learning for User Intent Understanding

Authors:

Chenliang LiAuthors Info & Claims

WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining

Pages 816 - 825

https://doi.org/10.1145/3616855.3635858

Published: 04 March 2024 Publication History

Abstract

In the world of E-Commerce, the core task is to understand the personalized preference from various kinds of heterogeneous information, such as textual reviews, item images and historical behaviors. In current systems, these heterogeneous information are mainly exploited to generate better item or user representations. For example, in scenario of visual search, the importance of modeling query image has been widely acknowledged. But, these existing solutions focus on improving the representation quality of the query image, overlooking the personalized visual preference of the user. Note that the visual features affect the user's decision significantly, e.g., the user could be more likely to click the items with her preferred design. Hence, it is fruitful to exploit the visual preference to deliver better capacity for personalization.

To this end, we propose a simple yet effective target-aware visual preference learning framework (named Tavern) for both item recommendation and search. The proposed Tavern works as an individual and generic model that can be smoothly plugged into different downstream systems. Specifically, for visual preference learning, we utilize the image of the target item to derive the visual preference signals for each historical clicked item. This procedure is modeled as a form of representation disentanglement, where the visual preference signals are extracted by taking off the noisy information irrelevant to visual preference from the shared visual information between the target and historical items. During this process, a novel selective orthogonality disentanglement is proposed to avoid the significant information loss. Then, a GRU network is utilized to aggregate these signals to form the final visual preference representation. Extensive experiments over three large-scale real-world datasets covering visual search, product search and recommendation well demonstrate the superiority of our proposed Tavern against existing technical alternatives. Further ablation study also confirms the validity of each design choice.

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Cited By

Qiu ZZhu JChen YCai GLiu WDong ZKing ISerra ESpezzano F(2024)EASE: Learning Lightweight Semantic Feature Adapters from Large Language Models for CTR PredictionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680048(4819-4827)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680048
Mao XChen ZHe ZJing YZhang KWang X(2024)HetFS: a method for fast similarity search with ad-hoc meta-paths on heterogeneous information networksWorld Wide Web10.1007/s11280-024-01303-127:6Online publication date: 18-Sep-2024
https://doi.org/10.1007/s11280-024-01303-1

Index Terms

Unified Visual Preference Learning for User Intent Understanding
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining

March 2024

1246 pages

ISBN:9798400703713

DOI:10.1145/3616855

General Chairs:
Luz Angélica
Caudillo Mata (MDA Geointelligence)
,
Silvio Lattanzi
Google Research
,
Andrés Muñoz Medina
Google Research
,
Program Chairs:
Leman Akoglu
CMU
,
Aristides Gionis
KTH
,
Sergei Vassilvitskii
Google Research

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Published: 04 March 2024

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WSDM '24: The 17th ACM International Conference on Web Search and Data Mining

March 4 - 8, 2024

Merida, Mexico

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Cited By

Qiu ZZhu JChen YCai GLiu WDong ZKing ISerra ESpezzano F(2024)EASE: Learning Lightweight Semantic Feature Adapters from Large Language Models for CTR PredictionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680048(4819-4827)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680048
Mao XChen ZHe ZJing YZhang KWang X(2024)HetFS: a method for fast similarity search with ad-hoc meta-paths on heterogeneous information networksWorld Wide Web10.1007/s11280-024-01303-127:6Online publication date: 18-Sep-2024
https://doi.org/10.1007/s11280-024-01303-1

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