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Joint Learning of E-commerce Search and Recommendation with a Unified Graph Neural Network

Published: 15 February 2022 Publication History

Abstract

Click-through rate (CTR) prediction plays an important role in search and recommendation, which are the two most prominent scenarios in e-commerce. A number of models have been proposed to predict CTR by mining user behaviors, especially users' interactions with items. But the sparseness of user behaviors is an obstacle to the improvement of CTR prediction. Previous works only focused on one scenario, either search or recommendation. However, on a practical e-commerce platform, search and recommendation share the same set of users and items, which means joint learning of both scenarios may alleviate the sparseness of user behaviors. In this paper, we propose a novel Search and Recommendation Joint Graph (SRJGraph) neural network to jointly learn a better CTR model for both scenarios. A key question of joint learning is how to effectively share information across search and recommendation, in spite of their differences. A notable difference between search and recommendation is that there are explicit queries in search, whereas no query exists in recommendation. We address this difference by constructing a unified graph to share representations of users and items across search and recommendation, as well as represent user-item interactions uniformly. In this graph, users and items are heterogeneous nodes, and search queries are incorporated into the user-item interaction edges as attributes. For recommendation where no query exists, a special attribute is attached on user-item interaction edges. We further propose an intention and upstream-aware aggregator to explore useful information from high-order connections among users and items. We conduct extensive experiments on a large-scale dataset collected from Taobao.com, the largest e-commerce platform in China. Empirical results show that SRJGraph significantly outperforms the state-of-the-art approaches of CTR prediction in both search and recommendation tasks.

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cover image ACM Conferences
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
February 2022
1690 pages
ISBN:9781450391320
DOI:10.1145/3488560
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 ACM 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: 15 February 2022

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Author Tags

  1. click-through rate prediction
  2. e-commerce
  3. graph neural network
  4. joint learning
  5. product search and recommendation

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  • (2024)Collaborative Sequential Recommendations via Multi-view GNN-transformersACM Transactions on Information Systems10.1145/364943642:6(1-27)Online publication date: 25-Jun-2024
  • (2024)Bridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other?Proceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688123(340-349)Online publication date: 8-Oct-2024
  • (2024)Unified Dual-Intent Translation for Joint Modeling of Search and RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671519(6291-6300)Online publication date: 25-Aug-2024
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  • (2024)Enhancing Click-through Rate Prediction in Recommendation Domain with Search Query RepresentationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679849(2462-2471)Online publication date: 21-Oct-2024
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  • (2024)UniSAR: Modeling User Transition Behaviors between Search and RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657811(1029-1039)Online publication date: 10-Jul-2024
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  • (2024)Exploring generative frameworks for product attribute value extractionExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122850243:COnline publication date: 25-Jun-2024
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