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RecGAN: recurrent generative adversarial networks for recommendation systems

Published: 27 September 2018 Publication History

Abstract

Recent studies in recommendation systems emphasize the significance of modeling latent features behind temporal evolution of user preference and item state to make relevant suggestions. However, static and dynamic behaviors and trends of users and items, which highly influence the feasibility of recommendations, were not adequately addressed in previous works. In this work, we leverage the temporal and latent feature modelling capabilities of Recurrent Neural Network (RNN) and Generative Adversarial Network (GAN), respectively, to propose a Recurrent Generative Adversarial Network (RecGAN). We use customized Gated Recurrent Unit (GRU) cells to capture latent features of users and items observable from short-term and long-term temporal profiles. The modification also includes collaborative filtering mechanisms to improve the relevance of recommended items. We evaluate RecGAN using two datasets on food and movie recommendation. Results indicate that our model outperforms other baseline models irrespective of user behavior and density of training data.

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Cited By

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  • (2025)Application of machine vision in food computing: A reviewFood Chemistry10.1016/j.foodchem.2024.141238463(141238)Online publication date: Jan-2025
  • (2024)Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion ModelsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679756(2618-2628)Online publication date: 21-Oct-2024
  • (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
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cover image ACM Conferences
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems
September 2018
600 pages
ISBN:9781450359016
DOI:10.1145/3240323
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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Publication History

Published: 27 September 2018

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Author Tags

  1. generative adversarial networks
  2. recommendation systems
  3. recurrent neural networks

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  • Short-paper

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  • BIGHEART

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RecSys '18
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RecSys '18: Twelfth ACM Conference on Recommender Systems
October 2, 2018
British Columbia, Vancouver, Canada

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RecSys '18 Paper Acceptance Rate 32 of 181 submissions, 18%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

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  • (2025)Application of machine vision in food computing: A reviewFood Chemistry10.1016/j.foodchem.2024.141238463(141238)Online publication date: Jan-2025
  • (2024)Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion ModelsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679756(2618-2628)Online publication date: 21-Oct-2024
  • (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)GAN-Based Multi-Task Learning Approach for Prognostics and Health Management of IIoTIEEE Transactions on Automation Science and Engineering10.1109/TASE.2023.326786021:3(2742-2762)Online publication date: Jul-2024
  • (2024)Design of a Recommendation System for an Efficient and Optimized (RSU) Usage and Protect from External Influences2024 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA)10.1109/ICECA63461.2024.10801040(1028-1032)Online publication date: 6-Nov-2024
  • (2024)Multi-Resolution Diffusion for Privacy-Sensitive Recommender SystemsIEEE Access10.1109/ACCESS.2024.338829912(58275-58287)Online publication date: 2024
  • (2024)Trust in AI-augmented design: Applying structural equation modeling to AI-augmented design acceptanceHeliyon10.1016/j.heliyon.2023.e2330510:1(e23305)Online publication date: Jan-2024
  • (2024)Improved negative sampling method in collaborative filtering recommendation based on Generative adversarial networkElectronic Commerce Research and Applications10.1016/j.elerap.2024.101412(101412)Online publication date: Jun-2024
  • (2024)USE OF CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS TO CREATE DEMOGRAPHIC COLLABORATIVE FILTERING DATASETSApplied Soft Computing10.1016/j.asoc.2024.112608(112608)Online publication date: Dec-2024
  • (2024)Recent trends in recommender systems: a surveyInternational Journal of Multimedia Information Retrieval10.1007/s13735-024-00349-113:4Online publication date: 10-Oct-2024
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