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Rating Augmentation with Generative Adversarial Networks towards Accurate Collaborative Filtering

Published: 13 May 2019 Publication History

Abstract

Generative Adversarial Networks (GAN) have not only achieved a big success in various generation tasks such as images, but also boosted the accuracy of classification tasks by generating additional labeled data, which is called data augmentation. In this paper, we propose a Rating Augmentation framework with GAN, named RAGAN, aiming to alleviate the data sparsity problem in collaborative filtering (CF), eventually improving recommendation accuracy significantly. We identify a unique challenge that arises when applying GAN to CF for rating augmentation: naive RAGAN tends to generate values biased towards high ratings. Then, we propose a refined version of RAGAN, named RAGANBT, which addresses this challenge successfully. Via our extensive experiments, we validate that our RAGANBT is really effective to solve the data sparsity problem, thereby providing existing CF models with great improvement in accuracy under various situations such as basic top-N recommendation, long-tail item recommendation, and recommendation to cold-start users.

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cover image ACM Other conferences
WWW '19: The World Wide Web Conference
May 2019
3620 pages
ISBN:9781450366748
DOI:10.1145/3308558
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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  • IW3C2: International World Wide Web Conference Committee

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New York, NY, United States

Publication History

Published: 13 May 2019

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Author Tags

  1. Collaborative filtering
  2. data augmentation
  3. data sparsity
  4. generative adversarial networks
  5. top-N recommendation

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  • Research-article
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  • Refereed limited

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WWW '19
WWW '19: The Web Conference
May 13 - 17, 2019
CA, San Francisco, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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  • (2024)Neural Click Models for Recommender SystemsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657939(2553-2558)Online publication date: 10-Jul-2024
  • (2024)Broad Recommender System: An Efficient Nonlinear Collaborative Filtering ApproachIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.33785998:4(2843-2857)Online publication date: Aug-2024
  • (2024)CF-MGAN: Collaborative filtering with metadata-aware generative adversarial networks for top-N recommendationInformation Sciences10.1016/j.ins.2024.121337(121337)Online publication date: Aug-2024
  • (2024)User Response Modeling in Recommender Systems: A SurveyJournal of Mathematical Sciences10.1007/s10958-024-07431-3Online publication date: 8-Nov-2024
  • (2024)Deep recommendation with iteration directional adversarial trainingComputing10.1007/s00607-024-01326-6106:10(3151-3174)Online publication date: 17-Jul-2024
  • (2023)Adversarial Neural Collaborative Filtering with Embedding Dimension CorrelationsData Intelligence10.1162/dint_a_001515:3(786-806)Online publication date: 1-Aug-2023
  • (2023)Bootstrapped Personalized Popularity for Cold Start Recommender SystemsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608820(715-722)Online publication date: 14-Sep-2023
  • (2023)Data-free Knowledge Distillation for Reusing Recommendation ModelsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608789(386-395)Online publication date: 14-Sep-2023
  • (2023)RESUS: Warm-up Cold Users via Meta-learning Residual User Preferences in CTR PredictionACM Transactions on Information Systems10.1145/356428341:3(1-26)Online publication date: 7-Feb-2023
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