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Adversarial Collaborative Neural Network for Robust Recommendation

Published: 18 July 2019 Publication History

Abstract

Most of recent neural network(NN)-based recommendation techniques mainly focus on improving the overall performance, such as hit ratio for top-N recommendation, where the users' feedbacks are considered as the ground-truth. In real-world applications, those feedbacks are possibly contaminated by imperfect user behaviours, posing challenges on the design of robust recommendation methods. Some methods apply man-made noises on the input data to train the networks more effectively (e.g. the collaborative denoising auto-encoder). In this work, we propose a general adversarial training framework for NN-based recommendation models, improving both the model robustness and the overall performance. We apply our approach on the collaborative auto-encoder model, and show that the combination of adversarial training and NN-based models outperforms highly competitive state-of-the-art recommendation methods on three public datasets.

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cover image ACM Conferences
SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2019
1512 pages
ISBN:9781450361729
DOI:10.1145/3331184
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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Publication History

Published: 18 July 2019

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Author Tags

  1. adversarial learning
  2. deep learning
  3. item recommendation

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SIGIR'19 Paper Acceptance Rate 84 of 426 submissions, 20%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%

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  • (2025)Uncertainty embedding of attribute networks based on multi-view information fusion and multi-order proximity preservationNeurocomputing10.1016/j.neucom.2024.129188620(129188)Online publication date: Mar-2025
  • (2025)Hyperbolic Adversarial Learning for Personalized Item RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5555-4_20(303-312)Online publication date: 12-Jan-2025
  • (2024)FINEST: Stabilizing Recommendations by Rank-Preserving Fine-TuningACM Transactions on Knowledge Discovery from Data10.1145/3695256Online publication date: 9-Sep-2024
  • (2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/36528913:2(1-68)Online publication date: 13-Apr-2024
  • (2024)Improving Adversarial Robustness for Recommendation Model via Cross-Domain Distributional Adversarial TrainingProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688116(278-286)Online publication date: 8-Oct-2024
  • (2024)Toward Adversarially Robust Recommendation From Adaptive Fraudster DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.332787619(907-919)Online publication date: 2024
  • (2024)Recommendation model based on knowledge graphs and semantic alignment2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825885(7242-7251)Online publication date: 15-Dec-2024
  • (2024)Modeling Item Exposure and User Satisfaction for Debiased Recommendation with Causal InferenceInformation Sciences10.1016/j.ins.2024.120834(120834)Online publication date: Jun-2024
  • (2024)Metric learning with adversarial hard negative samples for tag recommendationThe Journal of Supercomputing10.1007/s11227-024-06274-880:14(21475-21507)Online publication date: 11-Jun-2024
  • (2024)Robust Graph Recommendation via Noise-Aware Adversarial PerturbationDatabase Systems for Advanced Applications10.1007/978-981-97-5572-1_9(134-150)Online publication date: 31-Aug-2024
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