557 research outputs found
Deep Factorization Model for Robust Recommendation
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
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