{"type":"ScholarlyArticle","title":"Why We Go Where We Go: Profiling User Decisions on Choosing POIs","authors":[{"name":"Renjun Hu","slug":"renjun-hu","given":"Renjun","family":"Hu","affiliation":"SKLSDE Lab , Beihang University & Beijing Adv. Inno. Center for Big Data and Brain Computing"},{"name":"Xinjiang Lu","slug":"xinjiang-lu","given":"Xinjiang","family":"Lu","affiliation":"Business Intelligence Lab , Baidu Research 3 Business Analytics and Statistics , University of Tennessee , Knoxville"},{"name":"Chuanren Liu","slug":"chuanren-liu","given":"Chuanren","family":"Liu","affiliation":""},{"name":"Yanyan Li","slug":"yanyan-li","given":"Yanyan","family":"Li","affiliation":"Business Intelligence Lab , Baidu Research 3 Business Analytics and Statistics , University of Tennessee , Knoxville"},{"name":"Hao Liu","slug":"hao-liu","given":"Hao","family":"Liu","affiliation":"Business Intelligence Lab , Baidu Research 3 Business Analytics and Statistics , University of Tennessee , Knoxville"},{"name":"Jingjing Gu","slug":"jingjing-gu","given":"Jingjing","family":"Gu","affiliation":"Nanjing University of Aeronautics and Astronautics"},{"name":"Shuai Ma","slug":"shuai-ma","given":"Shuai","family":"Ma","affiliation":"SKLSDE Lab , Beihang University & Beijing Adv. Inno. Center for Big Data and Brain Computing"},{"name":"Hui Xiong","slug":"hui-xiong","given":"Hui","family":"Xiong","affiliation":"Rutgers University"}],"abstract":"While Point-of-Interest (POI) recommendation has been a popular topic of study for some time, little progress has been made for understanding why and how people make their decisions for the selection of POIs. To this end, in this paper, we propose a user decision profiling framework, named PROUD, which can identify the key factors in people's decisions on choosing POIs. Specifically, we treat each user decision as a set of factors and provide a method for learning factor embeddings. A unique perspective of our approach is to identify key factors, while preserving decision structures seamlessly, via a novel scalar projection maximization objective. Exactly solving the objective is non-trivial due to a sparsity constraint. To address this, our PROUD adopts a self projection attention and an L2 regularized sparse activation to directly estimate the likelihood of each factor to be a key factor. Finally, extensive experiments on real-world data validate the advantage of PROUD in preserving user decision structures. Also, our case study indicates that the identified key decision factors can help us to provide more interpretable recommendations and analyses.","doi":"10.24963/ijcai.2020/478","url":"https://ijcai.org/proceedings/2020/478","pdf":"https://ijcai.org/proceedings/2020/0478.pdf","markdown":"https://ijcai.org/proceedings/2020/478.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/478","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":478,"pages":{"first":3459,"last":3465},"keywords":["Data Mining: Applications","Multidisciplinary Topics and Applications: Ubiquitous Computing Systems","Multidisciplinary Topics and Applications: Other"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/26830/file_26830.m3u8","length_sec":514,"provider":"hls"}],"references":[{"position":1,"raw":"[Abdollahi and Nasraoui, 2017] Behnoush Abdollahi and Olfa Nasraoui. 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