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Exploring Missing Interactions: A Convolutional Generative Adversarial Network for Collaborative Filtering

Published: 19 October 2020 Publication History

Abstract

Adversarial examples can be detrimental to a recommender,leading to a surging enthusiasm for applying adversarial learning to improve recommendation performance, e.g. raising model robustness, alleviating data sparsity, generating initial profiles for cold-start users or items, etc. Most existing adversarial example generation methods fall within three categories: attacking the user-item interactions or auxiliary contents, adding perturbations in latent space, sampling the latent space according to certain distribution. In this work, we focus on the semantic-rich user-item interactions in a recommender system and propose a novel generative adversarial network (GAN) named Convolutional Generative Collaborative Filtering (Conv-GCF). We develop an effective perturbation mechanism (adversarial noise layer) for convolutional neural networks (CNN), based on which we design a generator with residual blocks to synthesize user-item interactions. We empirically demonstrate that on Conv-GCF, the adversarial noise layer is superior to the conventional noise-adding approach. Moreover, we propose two types of discriminators: one using Bayes Personalized Ranking (BPR) and the other with binary classification. On four public datasets, we show that our approach achieves the state-of-the-art top-n recommendation performance among competitive baselines.

Supplementary Material

MP4 File (3340531.3411917.mp4)
This video is for the full research paper, "Exploring Missing Interactions: A Convolutional Generative Adversarial Network for Collaborative Filtering", presented by Feng Yuan who is a PhD candidate from the University of New South Wales, Sydney, Australia. This paper focuses on adversarial recommendation model, where a novel adversarial noise adding scheme is proposed. Furthermore, two CNN-based GANs are designed to extract from the user-item interaction maps so that state-of-the-art Top-N recommendation results have been achieved. Finally, thorough ablation study on the proposed approach is conducted. In the video, the presenter starts from the motivation of this work, followed by a detailed introduction to the proposed approach and then, covers all the experiments in the paper.

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  • (2024)Robustness in Fairness Against Edge-Level Perturbations in GNN-Based RecommendationAdvances in Information Retrieval10.1007/978-3-031-56063-7_3(38-55)Online publication date: 23-Mar-2024
  • (2023)Robust multimedia recommender system based on dynamic collaborative filtering and directed adversarial learningInternational Journal of Machine Learning and Cybernetics10.1007/s13042-023-01868-914:11(3851-3865)Online publication date: 27-May-2023
  • (2023)Modeling users’ heterogeneous taste with diversified attentive user profilesUser Modeling and User-Adapted Interaction10.1007/s11257-023-09376-934:2(375-405)Online publication date: 1-Aug-2023
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cover image ACM Conferences
CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
October 2020
3619 pages
ISBN:9781450368599
DOI:10.1145/3340531
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: 19 October 2020

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Author Tags

  1. convolutional neural networks
  2. deep learning
  3. generative adversarial networks
  4. item recommendation

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Cited By

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  • (2024)Robustness in Fairness Against Edge-Level Perturbations in GNN-Based RecommendationAdvances in Information Retrieval10.1007/978-3-031-56063-7_3(38-55)Online publication date: 23-Mar-2024
  • (2023)Robust multimedia recommender system based on dynamic collaborative filtering and directed adversarial learningInternational Journal of Machine Learning and Cybernetics10.1007/s13042-023-01868-914:11(3851-3865)Online publication date: 27-May-2023
  • (2023)Modeling users’ heterogeneous taste with diversified attentive user profilesUser Modeling and User-Adapted Interaction10.1007/s11257-023-09376-934:2(375-405)Online publication date: 1-Aug-2023
  • (2023)Introduction to Deep LearningSustainable Computing10.1007/978-3-031-13577-4_15(253-267)Online publication date: 1-Jan-2023
  • (2022)DaisyRec 2.0: Benchmarking Recommendation for Rigorous EvaluationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.3231891(1-20)Online publication date: 2022
  • (2022)Conditional GAN based Collaborative Filtering with Data Augmentation for Cold-Start User2022 13th International Conference on Information and Communication Technology Convergence (ICTC)10.1109/ICTC55196.2022.9952471(1756-1761)Online publication date: 19-Oct-2022
  • (2021)The Idiosyncratic Effects of Adversarial Training on Bias in Personalized Recommendation LearningProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3478858(730-735)Online publication date: 13-Sep-2021
  • (2021)A Formal Analysis of Recommendation Quality of Adversarially-trained RecommendersProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482046(2852-2856)Online publication date: 26-Oct-2021
  • (2021)A Survey on Adversarial Recommender SystemsACM Computing Surveys10.1145/343972954:2(1-38)Online publication date: 5-Mar-2021

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