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Asymmetrical Attention Networks Fused Autoencoder for Debiased Recommendation

Authors:

Meng YuanAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology, Volume 14, Issue 6

Article No.: 100, Pages 1 - 24

https://doi.org/10.1145/3596498

Published: 14 November 2023 Publication History

Abstract

Popularity bias is a massive challenge for autoencoder-based models, which decreases the level of personalization and hurts the fairness of recommendations. User reviews reflect their preferences and help mitigate bias or unfairness in the recommendation. However, most existing works typically incorporate user (item) reviews into a long document and then use the same module to process the document in parallel. Actually, the set of user reviews is completely different from the set of item reviews. User reviews are heterogeneous in that they reflect a variety of items purchased by users, while item reviews are only related to the item itself and are thus typically homogeneous. In this article, a novel asymmetric attention network fused with autoencoders is proposed, which jointly learns representations from the user and item reviews and implicit feedback to perform recommendations. Specifically, we design an asymmetric attentive module to capture rich representations from user and item reviews, respectively, which solves data sparsity and explainable problems. Furthermore, to further address popularity bias, we apply a noise-contrastive estimation objective to learn high-quality “de-popularity” embedding via the decoder structure. A series of extensive experiments are conducted on four benchmark datasets to show that leveraging user review information can eliminate popularity bias and improve performance compared to various state-of-the-art recommendation techniques.

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Yi JChen Z(2024)Deconfounded Cross-modal Matching for Content-based Micro-video Background Music RecommendationACM Transactions on Intelligent Systems and Technology10.1145/365004215:3(1-25)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3650042
Li YChen HXu SGe YTan JLiu SZhang Y(2023)Fairness in Recommendation: Foundations, Methods, and ApplicationsACM Transactions on Intelligent Systems and Technology10.1145/361030214:5(1-48)Online publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1145/3610302
Madhusudhanan SJose ASahoo JMalekian R(2023)PRIMϵ: Novel Privacy-Preservation Model With Pattern Mining and Genetic AlgorithmIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.332476919(571-585)Online publication date: 16-Oct-2023
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Index Terms

Asymmetrical Attention Networks Fused Autoencoder for Debiased Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Top-k retrieval in databases
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 14, Issue 6

December 2023

493 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3632517

Editor:
Huan Liu
Arizona State University, USA

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Publication History

Published: 14 November 2023

Online AM: 17 May 2023

Accepted: 30 April 2023

Revised: 28 March 2023

Received: 27 June 2022

Published in TIST Volume 14, Issue 6

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Science and Technology Research Program of Chongqing Municipal Education Commission
Humanities and Social Sciences Research Program of Chongqing Municipal Education Commission
National Natural Science Foundation of China

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Cited By

Yi JChen Z(2024)Deconfounded Cross-modal Matching for Content-based Micro-video Background Music RecommendationACM Transactions on Intelligent Systems and Technology10.1145/365004215:3(1-25)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3650042
Li YChen HXu SGe YTan JLiu SZhang Y(2023)Fairness in Recommendation: Foundations, Methods, and ApplicationsACM Transactions on Intelligent Systems and Technology10.1145/361030214:5(1-48)Online publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1145/3610302
Madhusudhanan SJose ASahoo JMalekian R(2023)PRIMϵ: Novel Privacy-Preservation Model With Pattern Mining and Genetic AlgorithmIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.332476919(571-585)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1109/TIFS.2023.3324769
Zhao JHuang CWang WXie RDong RMatwin S(2023)Local differentially private federated learning with homomorphic encryptionThe Journal of Supercomputing10.1007/s11227-023-05378-x79:17(19365-19395)Online publication date: 27-May-2023
https://dl.acm.org/doi/10.1007/s11227-023-05378-x

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