Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107973
DC Field | Value | Language |
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dc.contributor | Department of Computing | - |
dc.creator | Chen, H | - |
dc.creator | Bei, Y | - |
dc.creator | Shen, Q | - |
dc.creator | Xu, Y | - |
dc.creator | Zhou, S | - |
dc.creator | Huang, W | - |
dc.creator | Huang, F | - |
dc.creator | Wang, S | - |
dc.creator | Huang, X | - |
dc.date.accessioned | 2024-07-22T07:30:42Z | - |
dc.date.available | 2024-07-22T07:30:42Z | - |
dc.identifier.isbn | 979-8-4007-0171-9 | - |
dc.identifier.uri | http://hdl.handle.net/10397/107973 | - |
dc.description | WWW '24: The ACM Web Conference 2024, Singapore, May 13 - 17, 2024 | en_US |
dc.language.iso | en | en_US |
dc.publisher | Association for Computing Machinery | en_US |
dc.rights | © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in WWW '24: Proceedings of the ACM on Web Conference 2024, https://doi.org/10.1145/3589334.3645517. | en_US |
dc.subject | Billion-scale online model | en_US |
dc.subject | Graph-based CTR prediction | en_US |
dc.subject | Next-generation recommendation model | en_US |
dc.title | Macro graph neural networks for online billion-scale recommender systems | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 3598 | - |
dc.identifier.epage | 3608 | - |
dc.identifier.doi | 10.1145/3589334.3645517 | - |
dcterms.abstract | Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-standing challenge for Graph Neural Networks (GNNs) due to the overwhelming computational complexity involved in aggregating billions of neighbors. To tackle this, GNN-based CTR models usually sample hundreds of neighbors out of the billions to facilitate efficient online recommendations. However, sampling only a small portion of neighbors results in a severe sampling bias and the failure to encompass the full spectrum of user or item behavioral patterns. To address this challenge, we name the conventional user-item recommendation graph as "micro recommendation grap" and introduce a revolutionizing MAcro Recommendation Graph (MAG) for billion-scale recommendations to reduce the neighbor count from billions to hundreds in the graph structure infrastructure. Specifically, We group micro nodes (users and items) with similar behavior patterns to form macro nodes and then MAG directly describes the relation between the user/item and the hundred of macro nodes rather than the billions of micro nodes. Subsequently, we introduce tailored Macro Graph Neural Networks (MacGNN) to aggregate information on a macro level and revise the embeddings of macro nodes. MacGNN has already served Taobao's homepage feed for two months, providing recommendations for over one billion users. Extensive offline experiments on three public benchmark datasets and an industrial dataset present that MacGNN significantly outperforms twelve CTR baselines while remaining computationally efficient. Besides, online A/B tests confirm MacGNN's superiority in billion-scale recommender systems. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | WWW '24 : Proceedings of the ACM on Web Conference 2024, p. 3598-3608 | - |
dcterms.issued | 2024 | - |
dc.identifier.scopus | 2-s2.0-85192642530 | - |
dc.relation.conference | International World Wide Web Conference [WWW] | - |
dc.description.validate | 202407 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a2922 | en_US |
dc.identifier.SubFormID | 48774 | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China (Grant No. 62272200, U22A2095, 61932010, 62172443) | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Conference Paper |
Files in This Item:
File | Description | Size | Format | |
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Chen_Macro_Graph_Neural.pdf | Pre-Published version | 2.04 MB | Adobe PDF | View/Open |
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