{"type":"ScholarlyArticle","title":"Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation","authors":[{"name":"Hao Wang","slug":"hao-wang","given":"Hao","family":"Wang","affiliation":"Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Huawei Shen","slug":"huawei-shen","given":"Huawei","family":"Shen","affiliation":"Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Wentao Ouyang","slug":"wentao-ouyang","given":"Wentao","family":"Ouyang","affiliation":"Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China"},{"name":"Xueqi Cheng","slug":"xueqi-cheng","given":"Xueqi","family":"Cheng","affiliation":"Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"}],"abstract":"Point-of-interest (POI) recommendation, i.e., recommending unvisited POIs for users, is a fundamental problem for location-based social networks. POI recommendation distinguishes itself from traditional item recommendation, e.g., movie recommendation, via geographical influence among POIs. Existing methods model the geographical influence between two POIs as the probability or propensity that the two POIs are co-visited by the same user given their physical distance. These methods assume that geographical influence between POIs is determined by their physical distance, failing to capture the asymmetry of geographical influence and the high variation of geographical influence across POIs. In this paper, we exploit POI-specific geographical influence to improve POI recommendation. We model the geographical influence between two POIs using three factors: the geo-influence of POI, the geo-susceptibility of POI, and their physical distance. Geo-influence captures POI?s capacity at exerting geographical influence to other POIs, and geo-susceptibility reflects POI?s propensity of being geographically influenced by other POIs. Experimental results on two real-world datasets demonstrate that POI-specific geographical influence significantly improves the performance of POI recommendation.","doi":"10.24963/ijcai.2018/539","url":"https://ijcai.org/proceedings/2018/539","pdf":"https://ijcai.org/proceedings/2018/0539.pdf","markdown":"https://ijcai.org/proceedings/2018/539.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/539","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":539,"pages":{"first":3877,"last":3883},"keywords":["Multidisciplinary Topics and Applications: Recommender Systems","Multidisciplinary Topics and Applications: AI and the Web"],"videos":[],"references":[{"position":1,"raw":"[Bao et al., 2012] Jie Bao, Yu Zheng, and Mohamed F Mok- bel. Location-based and preference-aware recommenda- tion using sparse geo-social networking data. In SIGSPA- TIAL, pages 199-208, 2012.","title":"Location-based and preference-aware recommendation using sparse geo-social networking data","authors":"Jie Bao, Yu Zheng, Mohamed F Mokbel","year":"2012","venue":"SIGSPA-TIAL","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cheng et al., 2012] Chen Cheng, Haiqin Yang, Irwin King, and Michael R Lyu. Fused matrix factorization with geo- graphical and social influence in location-based social net- works. In AAAI, pages 17-23, 2012.","title":"Fused matrix factorization with geographical and social influence in location-based social networks","authors":"Chen Cheng, Haiqin Yang, Irwin King, Michael R Lyu","year":"2012","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cho et al., 2011] Eunjoon Cho, Seth A. Myers, and Jure Leskovec. Friendship and mobility: User movement in location-based social networks. In KDD, pages 1082- 1090, 2011.","title":"Friendship and mobility: User movement in location-based social networks","authors":"Cho","year":"2011","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Feng et al., 2017] Shanshan Feng, Gao Cong, Bo An, and Yeow Meng Chee. Poi2vec: Geographical latent represen- tation for predicting future visitors. In AAAI, pages 102- 108, 2017.","title":"Poi2vec: Geographical latent representation for predicting future visitors","authors":"Shanshan Feng, Gao Cong, Bo An, Yeow Meng Chee","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Gao et al., 2015] Huiji Gao, Jiliang Tang, Xia Hu, and Huan Liu. Content-aware point of interest recommendation on location-based social networks. In AAAI, pages 1721- 1727, 2015.","title":"Content-aware point of interest recommendation on location-based social networks","authors":"Gao","year":"2015","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[He et al., 2016] Jing He, Xin Li, Lejian Liao, Dandan Song, and William K Cheung. Inferring a personalized next point-of-interest recommendation model with latent be- havior patterns. In AAAI, pages 137-143, 2016.","title":"Inferring a personalized next point-of-interest recommendation model with latent behavior patterns","authors":"Jing He, Xin Li, Lejian Liao, Dandan Song, William K Cheung","year":"2016","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Li et al., 2015] Xutao Li, Gao Cong, Xiao-Li Li, Tuan- Anh Nguyen Pham, and Shonali Krishnaswamy. Rank- geofm: A ranking based geographical factorization method for point of interest recommendation. In SIGIR, pages 433-442, 2015.","title":"Rankgeofm: A ranking based geographical factorization method for point of interest recommendation","authors":"Xutao Li, Gao Cong, Xiao-Li Li, Tuan-Anh Nguyen Pham, Shonali Krishnaswamy","year":"2015","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Li et al., 2016] Huayu Li, Yong Ge, Richang Hong, and Hengshu Zhu. Point-of-interest recommendations: Learn- ing potential check-ins from friends. In KDD, pages 975- 984, 2016.","title":"Point-of-interest recommendations: Learning potential check-ins from friends","authors":"Li","year":"2016","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Li et al., 2017] Huayu Li, Yong Ge, Defu Lian, and Hao Liu. Learning user's intrinsic and extrinsic interests for point-of-interest recommendation: A unified approach. In IJCAI, 2017.","title":"Learning user's intrinsic and extrinsic interests for point-of-interest recommendation: A unified approach","authors":"Huayu Li, Yong Ge, Defu Lian, Hao Liu","year":"2017","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2017/294"},{"position":10,"raw":"[Lian et al., 2014] Defu Lian, Cong Zhao, Xing Xie, Guangzhong Sun, Enhong Chen, and Yong Rui. Geomf: joint geographical modeling and matrix factorization for point-of-interest recommendation. In KDD, pages 831- 840, 2014.","title":"Geomf: joint geographical modeling and matrix factorization for point-of-interest recommendation","authors":"Defu Lian, Cong Zhao, Xing Xie, Guangzhong Sun, Enhong Chen, Yong Rui","year":"2014","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Liu and Xiong, 2013] Bin Liu and Hui Xiong. Point-of- interest recommendation in location based social networks with topic and location awareness. In ICDM, pages 396- 404, 2013.","title":"Point-ofinterest recommendation in location based social networks with topic and location awareness","authors":"Xiong ; ] Bin Liu, Hui Liu, Xiong","year":"2013","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Liu et al., 2014] Yong Liu, Wei Wei, Aixin Sun, and Chun- yan Miao. Exploiting geographical neighborhood char- acteristics for location recommendation. In CIKM, pages 739-748, 2014.","title":"Exploiting geographical neighborhood characteristics for location recommendation","authors":"Yong Liu, Wei Wei, Aixin Sun, Chunyan Miao","year":"2014","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Liu et al., 2017] Yiding Liu, Tuan-Anh Nguyen Pham, Gao Cong, and Quan Yuan. An experimental evaluation of point-of-interest recommendation in location-based social networks. VLDB, 10(10):1010-1021, 2017.","title":"An experimental evaluation of point-of-interest recommendation in location-based social networks","authors":"Liu","year":"2017","venue":"VLDB","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Mikolov et al., 2013] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed rep- resentations of words and phrases and their composition- ality. In NIPS, pages 3111-3119, 2013.","title":"Distributed representations of words and phrases and their compositionality","authors":"Mikolov","year":"2013","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Saleem et al., 2017] Muhammad Aamir Saleem, Rohit Ku- mar, Toon Calders, Xike Xie, and Torben Bach Pedersen. Location influence in location-based social networks. In WSDM, pages 621-630, 2017.","title":"Location influence in location-based social networks","authors":"Saleem","year":"2017","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Sun et al., 2017] Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Yu Chen, and Chi Xu. Mrlr: Multi-level repre- sentation learning for personalized ranking in recommen- dation. In IJCAI, 2017.","title":"Mrlr: Multi-level representation learning for personalized ranking in recommendation","authors":"Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Yu Chen, Chi Xu","year":"2017","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2017/391"},{"position":17,"raw":"[Tang et al., 2013] Jiliang Tang, Xia Hu, Huiji Gao, and Huan Liu. Exploiting local and global social context for recommendation. In IJCAI, pages 2712-2718, 2013.","title":"Exploiting local and global social context for recommendation","authors":"Jiliang Tang, Xia Hu, Huiji Gao, Huan Liu","year":"2013","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2013/399"},{"position":18,"raw":"[Wang et al., 2015a] Weiqing Wang, Hongzhi Yin, Ling Chen, Yizhou Sun, Shazia Sadiq, and Xiaofang Zhou. Geo-sage: A geographical sparse additive generative model for spatial item recommendation. In KDD, pages 1255-1264, 2015.","title":"Geo-sage: A geographical sparse additive generative model for spatial item recommendation","authors":"Wang","year":"2015","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Wang et al., 2015b] Yongqing Wang, Huawei Shen, Shenghua Liu, and Xueqi Cheng. Learning user-specific latent influence and susceptibility from information cascades. In AAAI, 2015.","title":"Learning user-specific latent influence and susceptibility from information cascades","authors":"Wang","year":"2015","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Xiao et al., 2010] Xiangye Xiao, Yu Zheng, Qiong Luo, and Xing Xie. Finding similar users using category-based lo- cation history. In SIGSPATIAL, pages 442-445, 2010.","title":"Finding similar users using category-based location history","authors":"Xiangye Xiao, Yu Zheng, Qiong Luo, Xing Xie","year":"2010","venue":"SIGSPATIAL","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Xie et al., 2016] Min Xie, Hongzhi Yin, Hao Wang, Fan- jiang Xu, Weitong Chen, and Sen Wang. Learning graph- based poi embedding for location-based recommendation. In CIKM, pages 15-24, 2016.","title":"Learning graphbased poi embedding for location-based recommendation","authors":"Xie","year":"2016","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Ye et al., 2011] Mao Ye, Peifeng Yin, Wang-Chien Lee, and Dik-Lun Lee. Exploiting geographical influence for col- laborative point-of-interest recommendation. In SIGIR, pages 325-334, 2011.","title":"Exploiting geographical influence for collaborative point-of-interest recommendation","authors":"Mao Ye, Peifeng Yin, Wang-Chien Lee, Dik-Lun Lee","year":"2011","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Yuan et al., 2013] Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, and Nadia Magnenat Thalmann. Time-aware point-of-interest recommendation. In SIGIR, pages 363- 372, 2013.","title":"Time-aware point-of-interest recommendation","authors":"Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, Nadia Magnenat Thalmann","year":"2013","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Zhang and Chow, 2013] Jia-Dong Zhang and Chi-Yin Chow. igslr: personalized geo-social location recom- mendation: a kernel density estimation approach. In SIGSPATIAL, pages 334-343, 2013.","title":"igslr: personalized geo-social location recommendation: a kernel density estimation approach","authors":"Chow ; Jia-Dong Zhang, Chi-Yin Zhang, Chow","year":"2013","venue":"SIGSPATIAL","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zhang and Chow, 2015] Jia-Dong Zhang and Chi-Yin Chow. Geosoca: Exploiting geographical, social and categorical correlations for point-of-interest recommenda- tions. In SIGIR, pages 443-452, 2015.","title":"Geosoca: Exploiting geographical, social and categorical correlations for point-of-interest recommendations","authors":"Chow ; Jia-Dong Zhang, Chi-Yin Zhang, Chow","year":"2015","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhang et al., 2016] Fuzheng Zhang, Nicholas Jing Yuan, Kai Zheng, Defu Lian, Xing Xie, and Yong Rui. Exploit- ing dining preference for restaurant recommendation. In WWW, pages 725-735, 2016.","title":"Exploiting dining preference for restaurant recommendation","authors":"Zhang","year":"2016","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Zhao et al., 2017] Shenglin Zhao, Tong Zhao, Irwin King, and Michael R Lyu. Geo-teaser: Geo-temporal sequential embedding rank for point-of-interest recommendation. In WWW, pages 153-162, 2017.","title":"Geo-teaser: Geo-temporal sequential embedding rank for point-of-interest recommendation","authors":"Zhao","year":"2017","venue":"WWW","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}