Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality
Vito Walter Anelli,
Yashar Deldjoo,
Tommaso Di Noia
et al.
Abstract:Recommender systems (RSs) employ user-item feedback, e.g., ratings, to match customers to personalized lists of products. Approaches to top-k recommendation mainly rely on Learning-To-Rank algorithms and, among them, the most widely adopted is Bayesian Personalized Ranking (BPR), which bases on a pair-wise optimization approach. Recently, BPR has been found vulnerable against adversarial perturbations of its model parameters. Adversarial Personalized Ranking (APR) mitigates this issue by robustifying BPR via a… Show more
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