A survey on adversarial recommender systems: from attack/defense strategies to generative adversarial networks
Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization
(MF) and deep CF methods, are widely used in modern recommender systems (RS) due to …
(MF) and deep CF methods, are widely used in modern recommender systems (RS) due to …
Movie popularity and target audience prediction using the content-based recommender system
The movie is one of the integral components of our everyday entertainment. The worldwide
movie industry is one of the most growing and significant industries and seizing the attention …
movie industry is one of the most growing and significant industries and seizing the attention …
Adversarial machine learning in recommender systems (aml-recsys)
Recommender systems (RS) are an integral part of many online services aiming to provide
an enhanced user-oriented experience. Machine learning (ML) models are nowadays …
an enhanced user-oriented experience. Machine learning (ML) models are nowadays …
Adversarial Machine Learning for Recommendation Systems
AA Shah - 2022 - search.proquest.com
Abstract Recently, Generative Adversarial Networks (GANs) have been applied to the
problem of Cold-Start Recommendation, but the training performance of these models is …
problem of Cold-Start Recommendation, but the training performance of these models is …
[PDF][PDF] Building Up Recommender Systems By Deep Learning For Cognitive Services
F Yuan - 2021 - unsworks.unsw.edu.au
Cognitive services provide artificial intelligence (AI) technology for application developers,
who are not required to be experts on machine learning. Cognitive services are presented …
who are not required to be experts on machine learning. Cognitive services are presented …
An Overview of Recommendation System Based on Generative Adversarial Networks
B Du, L Tang, L Liu - Proceedings of the 2020 3rd International …, 2020 - dl.acm.org
With the growth of explosive information resources, information overload has become
increasingly serious. Users are eager to find what they need in a large amount of information …
increasingly serious. Users are eager to find what they need in a large amount of information …