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GAN-based matrix factorization for recommender systems

Published: 06 May 2022 Publication History

Abstract

Proposed in 2014, Generative Adversarial Networks (GAN) initiated a fresh interest in generative modelling. They immediately achieved state-of-the-art in image synthesis, image-to-image translation, text-to-image generation, image inpainting and have been used in sciences ranging from medicine to high-energy particle physics. Despite their popularity and ability to learn arbitrary distributions, GAN have not been widely applied in recommender systems (RS). Moreover, only few of the techniques that have introduced GAN in RS have employed them directly as a collaborative filtering (CF) model.
In this work we propose a new GAN-based approach that learns user and item latent factors in a matrix factorization setting for the generic top-N recommendation problem. Following the vector-wise GAN training approach for RS introduced by CFGAN, we identify 2 unique issues when utilizing GAN for CF. We propose solutions for both of them by using an autoencoder as discriminator and incorporating an additional loss function for the generator. We evaluate our model, GANMF, through well-known datasets in the RS community and show improvements over traditional CF approaches and GAN-based models. Through an ablation study on the components of GANMF we aim to understand the effects of our architectural choices. Finally, we provide a qualitative evaluation of the matrix factorization performance of GANMF.

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cover image ACM Conferences
SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing
April 2022
2099 pages
ISBN:9781450387132
DOI:10.1145/3477314
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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Published: 06 May 2022

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Author Tags

  1. autoencoder
  2. collaborative filtering
  3. feature matching
  4. generative adversarial networks
  5. matrix factorization

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  • (2024)Advancements in Recommender Systems Through the Integration of Generative Adversarial NetworksInternational Journal of Informatics and Applied Mathematics10.53508/ijiam.14064986:2(35-45)Online publication date: 29-Jan-2024
  • (2024)Recommendation systems techniques based on generative models and matrix factorization: a surveyMathematical Modeling and Computing10.23939/mmc2024.04.107811:4(1078-1092)Online publication date: 2024
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