{"type":"ScholarlyArticle","title":"Geo-ALM: POI Recommendation by Fusing Geographical Information and Adversarial Learning Mechanism","authors":[{"name":"Wei Liu","slug":"wei-liu","given":"Wei","family":"Liu","affiliation":"Guangdong Key Lab. of Big Data Anal. and Proc. , Sun Yat-Sen University , Guangzhou , China; National Engineering Laboratory for Big Data Analysis and Applications , Beijing , China"},{"name":"Zhi-Jie Wang","slug":"zhi-jie-wang","given":"Zhi-Jie","family":"Wang","affiliation":"Guangdong Key Lab. of Big Data Anal. and Proc. , Sun Yat-Sen University , Guangzhou , China; National Engineering Laboratory for Big Data Analysis and Applications , Beijing , China"},{"name":"Bin Yao","slug":"bin-yao","given":"Bin","family":"Yao","affiliation":"Department of Comp. Sci. and Eng. , Shanghai Jiao Tong University , Shanghai , China"},{"name":"Jian Yin","slug":"jian-yin","given":"Jian","family":"Yin","affiliation":"Guangdong Key Lab. of Big Data Anal. and Proc. , Sun Yat-Sen University , Guangzhou , China; National Engineering Laboratory for Big Data Analysis and Applications , Beijing , China"}],"abstract":"Learning user’s preference from check-in data is\r\nimportant for POI recommendation. Yet, a user\r\nusually has visited some POIs while most of POIs\r\nare unvisited (i.e., negative samples). To leverage\r\nthese “no-behavior” POIs, a typical approach\r\nis pairwise ranking, which constructs ranking pairs\r\nfor the user and POIs. Although this approach is\r\ngenerally effective, the negative samples in ranking\r\npairs are obtained randomly, which may fail to\r\nleverage “critical” negative samples in the model\r\ntraining. On the other hand, previous studies also\r\nutilized geographical feature to improve the recommendation\r\nquality. Nevertheless, most of previous\r\nworks did not exploit geographical information\r\ncomprehensively, which may also affect the performance.\r\nTo alleviate these issues, we propose a geographical\r\ninformation based adversarial learning\r\nmodel (Geo-ALM), which can be viewed as a fusion\r\nof geographic features and generative adversarial\r\nnetworks. Its core idea is to learn the discriminator\r\nand generator interactively, by exploiting two\r\ngranularity of geographic features (i.e., region and\r\nPOI features). Experimental results show that Geo-\r\nALM can achieve competitive performance, compared\r\nto several state-of-the-arts.","doi":"10.24963/ijcai.2019/250","url":"https://ijcai.org/proceedings/2019/250","pdf":"https://ijcai.org/proceedings/2019/0250.pdf","markdown":"https://ijcai.org/proceedings/2019/250.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/250","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":"Knowledge Representation and Reasoning","track":"Main track","number":250,"pages":{"first":1807,"last":1813},"keywords":["Multidisciplinary Topics and Applications: Transportation","Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning","Machine Learning: Probabilistic Machine Learning","Machine Learning: Deep Learning","Machine Learning: Feature Selection ; Learning Sparse Models","Machine Learning: Recommender Systems","Multidisciplinary Topics and Applications: Databases"],"videos":[],"references":[{"position":1,"raw":"[Bao et al., 2015] Jie Bao, Yu Zheng, David Wilkie, and Mo- hamed Mokbel. 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