{"type":"ScholarlyArticle","title":"PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation","authors":[{"name":"Qiong Wu","slug":"qiong-wu","given":"Qiong","family":"Wu","affiliation":"Alibaba-NTU Singapore Joint Research Institute; The Joint NTU - UBC Research Centre of Excellence in Active Living for the Elderly (LILY)"},{"name":"Yong Liu","slug":"yong-liu","given":"Yong","family":"Liu","affiliation":"Alibaba-NTU Singapore Joint Research Institute; The Joint NTU - UBC Research Centre of Excellence in Active Living for the Elderly (LILY)"},{"name":"Chunyan Miao","slug":"chunyan-miao","given":"Chunyan","family":"Miao","affiliation":"Alibaba-NTU Singapore Joint Research Institute; The Joint NTU - UBC Research Centre of Excellence in Active Living for the Elderly (LILY); School of Computer Science and Engineering , Nanyang Technological University"},{"name":"Binqiang Zhao","slug":"binqiang-zhao","given":"Binqiang","family":"Zhao","affiliation":"Alibaba Group"},{"name":"Yin Zhao","slug":"yin-zhao","given":"Yin","family":"Zhao","affiliation":"Alibaba Group"},{"name":"Lu Guan","slug":"lu-guan","given":"Lu","family":"Guan","affiliation":"Alibaba Group"}],"abstract":"This paper proposes Personalized Diversity-promoting GAN (PD-GAN), a novel recommendation model to generate diverse, yet relevant recommendations. Specifically, for each user, a generator recommends a set of diverse and relevant items by sequentially sampling from a personalized Determinantal Point Process (DPP) kernel matrix. This kernel matrix is constructed by two learnable components: the general co-occurrence of diverse items and the user's personal preference to items. To learn the first component, we propose a novel pairwise learning paradigm using training pairs, and each training pair consists of a set of diverse items and a set of similar items randomly sampled from the observed data of all users. The second component is learnt through adversarial training against a discriminator which strives to distinguish between recommended items and the ground-truth sets randomly sampled from the observed data of the target user. Experimental results show that PD-GAN is superior to generate recommendations that are both diverse and relevant.","doi":"10.24963/ijcai.2019/537","url":"https://ijcai.org/proceedings/2019/537","pdf":"https://ijcai.org/proceedings/2019/0537.pdf","markdown":"https://ijcai.org/proceedings/2019/537.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/537","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":537,"pages":{"first":3870,"last":3876},"keywords":["Multidisciplinary Topics and Applications: Recommender Systems","Machine Learning: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Adomavicius and Kwon, 2012] Gediminas Adomavicius and YoungOk Kwon. 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