{"type":"ScholarlyArticle","title":"Contextualized Point-of-Interest Recommendation","authors":[{"name":"Peng Han","slug":"peng-han","given":"Peng","family":"Han","affiliation":"King Abdullah University of Science and Technology"},{"name":"Zhongxiao Li","slug":"zhongxiao-li","given":"Zhongxiao","family":"Li","affiliation":"King Abdullah University of Science and Technology"},{"name":"Yong Liu","slug":"yong-liu","given":"Yong","family":"Liu","affiliation":"Nanyang Technological University"},{"name":"Peilin Zhao","slug":"peilin-zhao","given":"Peilin","family":"Zhao","affiliation":"Tencent AI Lab"},{"name":"Jing Li","slug":"jing-li","given":"Jing","family":"Li","affiliation":"Inception Institute of Artificial Intelligence"},{"name":"Hao Wang","slug":"hao-wang","given":"Hao","family":"Wang","affiliation":"Inception Institute of Artificial Intelligence"},{"name":"Shuo Shang","slug":"shuo-shang","given":"Shuo","family":"Shang","affiliation":"University of Electronic Science and Technology of China"}],"abstract":"Point-of-interest (POI) recommendation has become an increasingly important sub-field of recommendation system research. Previous methods employ various assumptions to exploit the contextual information for improving the recommendation accuracy. The common property among them is that similar users are more likely to visit similar POIs and similar POIs would like to be visited by the same user. However, none of existing methods utilize similarity explicitly to make recommendations. In this paper, we propose a new framework for POI recommendation, which explicitly utilizes similarity with contextual information. Specifically, we categorize the context information into two groups, i.e.,  global and local context, and develop different regularization terms to incorporate them for recommendation. A graph Laplacian regularization term is utilized to exploit the global context information. Moreover, we cluster users into different groups, and let the objective function constrain the users in the same group to have similar predicted POI ratings. An alternating optimization method is developed to optimize our model and get the final rating matrix. The results in our experiments show that our algorithm outperforms all the state-of-the-art methods.","doi":"10.24963/ijcai.2020/344","url":"https://ijcai.org/proceedings/2020/344","pdf":"https://ijcai.org/proceedings/2020/0344.pdf","markdown":"https://ijcai.org/proceedings/2020/344.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/344","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":"Machine Learning","track":"Main track","number":344,"pages":{"first":2484,"last":2490},"keywords":["Data Mining: Mining Spatial, Temporal Data","Machine Learning: Recommender Systems","Multidisciplinary Topics and Applications: Transportation"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/26653/file_26653.m3u8","length_sec":272,"provider":"hls"}],"references":[{"position":1,"raw":"[Cheng et al., 2012] Chen Cheng, Haiqin Yang, Irwin King, and Michael R. 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