{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:03:08Z","timestamp":1775815388809,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/537","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"3870-3876","source":"Crossref","is-referenced-by-count":41,"title":["PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation"],"prefix":"10.24963","author":[{"given":"Qiong","family":"Wu","sequence":"first","affiliation":[{"name":"Alibaba-NTU Singapore Joint Research Institute"},{"name":"The Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY)"}]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[{"name":"Alibaba-NTU Singapore Joint Research Institute"},{"name":"The Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY)"}]},{"given":"Chunyan","family":"Miao","sequence":"additional","affiliation":[{"name":"Alibaba-NTU Singapore Joint Research Institute"},{"name":"The Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY)"},{"name":"School of Computer Science and Engineering, Nanyang Technological University"}]},{"given":"Binqiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Yin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Lu","family":"Guan","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:49:59Z","timestamp":1564300199000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/537"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/537","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}