GAN-based Matrix Factorization for Recommender Systems

by   Ervin Dervishaj, et al.

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.



There are no comments yet.


page 7


Adversarial Machine Learning in Recommender Systems: State of the art and Challenges

Latent-factor models (LFM) based on collaborative filtering (CF), such a...

Learning with Heterogeneous Side Information Fusion for Recommender Systems

Recommender System (RS) is a hot area where artificial intelligence (AI)...

Application of WGAN-GP in recommendation and Questioning the relevance of GAN-based approaches

Many neural-based recommender systems were proposed in recent years and ...

Providing reliability in Recommender Systems through Bernoulli Matrix Factorization

Recommender Systems are giving increasing importance to the beyond accur...

A Distributed Real-Time Recommender System for Big Data Streams

In today's data-driven world, recommender systems (RS) play a crucial ro...

From Personalization to Privatization: Meta Matrix Factorization for Private Rating Predictions

Matrix factorization (MF) techniques have been shown to be effective for...

Feature Quantization Improves GAN Training

The instability in GAN training has been a long-standing problem despite...

Code Repositories


This is the repository for our paper accepted at ACM/SIGAPP Symposium on Applied Computing (SAC '22) "GAN-based Matrix Factorization for Recommender Systems".

view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.