{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/adversarial-personalized-ranking-for","title":"Adversarial Personalized Ranking for Recommendation","arxiv_id":"1808.03908","date":"2018-08-12","proceeding":null,"authors":["Xiangnan He","Zhankui He","Xiaoyu Du","Tat-Seng Chua"],"abstract":"Item recommendation is a personalized ranking task. To this end, many\nrecommender systems optimize models with pairwise ranking objectives, such as\nthe Bayesian Personalized Ranking (BPR). Using matrix Factorization (MF) ---\nthe most widely used model in recommendation --- as a demonstration, we show\nthat optimizing it with BPR leads to a recommender model that is not robust. In\nparticular, we find that the resultant model is highly vulnerable to\nadversarial perturbations on its model parameters, which implies the possibly\nlarge error in generalization.\n  To enhance the robustness of a recommender model and thus improve its\ngeneralization performance, we propose a new optimization framework, namely\nAdversarial Personalized Ranking (APR). In short, our APR enhances the pairwise\nranking method BPR by performing adversarial training. It can be interpreted as\nplaying a minimax game, where the minimization of the BPR objective function\nmeanwhile defends an adversary, which adds adversarial perturbations on model\nparameters to maximize the BPR objective function. To illustrate how it works,\nwe implement APR on MF by adding adversarial perturbations on the embedding\nvectors of users and items. Extensive experiments on three public real-world\ndatasets demonstrate the effectiveness of APR --- by optimizing MF with APR, it\noutperforms BPR with a relative improvement of 11.2% on average and achieves\nstate-of-the-art performance for item recommendation. Our implementation is\navailable at: https://github.com/hexiangnan/adversarial_personalized_ranking.","url_abs":"http://arxiv.org/abs/1808.03908v1","url_pdf":"http://arxiv.org/pdf/1808.03908v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"adversarial-personalized-ranking-for","repo_url":"https://github.com/hexiangnan/adversarial_personalized_ranking","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}