Recently, malevolent user hacking has become a huge problem for real-world companies. In order to learn predictive models for recommender systems, factorization techniques have been developed to deal with user-item ratings. In this paper, we suggest a broad architecture of a factorization model with adversarial training to get over these issues. The effectiveness of our systems is demonstrated by experimental findings on real-world datasets.
@article{arxiv.2211.02894,
title = {Deep Factorization Model for Robust Recommendation},
author = {Li Wang and Qiang Zhao and Wei Wang},
journal= {arXiv preprint arXiv:2211.02894},
year = {2022}
}