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Learning Representations of Inactive Users: A Cross Domain Approach with Graph Neural Networks

Published: 30 October 2021 Publication History

Abstract

Understanding inactive users is the key to user growth and engagement for many Internet companies. However, learning inactive users' representations and their preferences is still challenging because the features available are missing and the positive responses or labels are insufficient. In this paper, we propose a cross domain learning approach to exclusively recommend customized items to inactive users by leveraging the knowledge of active users. Particularly, we represent users, no matter active or inactive users, by their friends' browsing behaviors based on a graph neural network (GNN) layer atop of a heterogeneous graph defined on social networks (user-user friendships) and browsing behaviors (user-page clicks). We jointly optimize the learning tasks of active users in source domain and inactive users in target domain based on the domain invariant features extracted from the embedding of our GNN layer, where the domain invariant features that are learned to benefit both tasks on active/inactive users, and are indiscriminate with respect to the shift between the domains. Extensive experiments show that our approach can well capture the preference of inactive users using both public data and real-world data at Alipay.

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Cited By

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  • (2024)Federated Learning-Based Social Recommendation with Social Denoising2024 International Conference on New Trends in Computational Intelligence (NTCI)10.1109/NTCI64025.2024.10776360(331-335)Online publication date: 18-Oct-2024
  • (2024)Learning Social Graph for Inactive User RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5572-1_10(151-167)Online publication date: 31-Aug-2024
  • (2023)Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge TransferProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614796(99-109)Online publication date: 21-Oct-2023
  • Show More Cited By

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cover image ACM Conferences
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management
October 2021
4966 pages
ISBN:9781450384469
DOI:10.1145/3459637
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: 30 October 2021

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Author Tags

  1. cross domain recommendation
  2. graph neural networks

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Cited By

View all
  • (2024)Federated Learning-Based Social Recommendation with Social Denoising2024 International Conference on New Trends in Computational Intelligence (NTCI)10.1109/NTCI64025.2024.10776360(331-335)Online publication date: 18-Oct-2024
  • (2024)Learning Social Graph for Inactive User RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5572-1_10(151-167)Online publication date: 31-Aug-2024
  • (2023)Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge TransferProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614796(99-109)Online publication date: 21-Oct-2023
  • (2023)Preference-aware Graph Attention Networks for Cross-Domain Recommendations with Collaborative Knowledge GraphACM Transactions on Information Systems10.1145/357692141:3(1-26)Online publication date: 7-Feb-2023
  • (2022)DisenCDRProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531967(267-277)Online publication date: 6-Jul-2022

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