{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T05:06:19Z","timestamp":1769835979205,"version":"3.49.0"},"publisher-location":"New York, New York, USA","reference-count":35,"publisher":"ACM Press","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1145\/3041021.3055130","type":"proceedings-article","created":{"date-parts":[[2018,1,11]],"date-time":"2018-01-11T18:39:25Z","timestamp":1515695965000},"page":"993-1002","source":"Crossref","is-referenced-by-count":29,"title":["Restaurant Survival Analysis with Heterogeneous Information"],"prefix":"10.1145","author":[{"given":"Jianxun","family":"Lian","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"given":"Fuzheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Microsoft Research, Beijing, China"}]},{"given":"Xing","family":"Xie","sequence":"additional","affiliation":[{"name":"Microsoft Research, Beijing, China"}]},{"given":"Guangzhong","family":"Sun","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]}],"member":"320","reference":[{"key":"key-10.1145\/3041021.3055130-1","unstructured":"A. Agarwal, E. Akchurin, C. Basoglu, G. Chen, S. Cyphers, J. Droppo, A. Eversole, B. Guenter, M. Hillebrand, R. Hoens, et al. An introduction to computational networks and the computational network toolkit. Technical report."},{"key":"key-10.1145\/3041021.3055130-2","doi-asserted-by":"crossref","unstructured":"J.-H. Ahn, S.-P. Han, and Y.-S. Lee. Customer churn analysis: Churn determinants and mediation effects of partial defection in the korean mobile telecommunications service industry. Telecommunications policy, 30(10):552--568, 2006.","DOI":"10.1016\/j.telpol.2006.09.006"},{"key":"key-10.1145\/3041021.3055130-3","doi-asserted-by":"crossref","unstructured":"H. Amiri and H. Daumé III. Short text representation for detecting churn in microblogs. In Thirtieth AAAI Conference on Artificial Intelligence, 2016.","DOI":"10.1609\/aaai.v30i1.10333"},{"key":"key-10.1145\/3041021.3055130-4","doi-asserted-by":"crossref","unstructured":"S. Bakhshi, P. Kanuparthy, and E. Gilbert. Demographics, weather and online reviews: A study of restaurant recommendations. In Proceedings of the 23rd International Conference on World Wide Web, WWW '14, pages 443--454, New York, NY, USA, 2014. ACM.","DOI":"10.1145\/2566486.2568021"},{"key":"key-10.1145\/3041021.3055130-5","unstructured":"D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. the Journal of machine Learning research, 3:993--1022, 2003."},{"key":"key-10.1145\/3041021.3055130-6","doi-asserted-by":"crossref","unstructured":"C. Buckley and E. M. Voorhees. Retrieval evaluation with incomplete information. In Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pages 25--32. ACM, 2004.","DOI":"10.1145\/1008992.1009000"},{"key":"key-10.1145\/3041021.3055130-7","unstructured":"T. Chen and C. Guestrin. Xgboost: A scalable tree boosting system. CoRR, abs\/1603.02754, 2016."},{"key":"key-10.1145\/3041021.3055130-8","doi-asserted-by":"crossref","unstructured":"G. Dror, D. Pelleg, O. Rokhlenko, and I. Szpektor. Churn prediction in new users of yahoo! answers. In Proceedings of the 21st international conference companion on World Wide Web, pages 829--834. ACM, 2012.","DOI":"10.1145\/2187980.2188207"},{"key":"key-10.1145\/3041021.3055130-9","doi-asserted-by":"crossref","unstructured":"J. H. Friedman. Greedy function approximation: a gradient boosting machine. Annals of statistics, pages 1189--1232, 2001.","DOI":"10.1214\/aos\/1013203451"},{"key":"key-10.1145\/3041021.3055130-10","doi-asserted-by":"crossref","unstructured":"Y. Fu, Y. Ge, Y. Zheng, Z. Yao, Y. Liu, H. Xiong, and J. Yuan. Sparse real estate ranking with online user reviews and offline moving behaviors. In 2014 IEEE International Conference on Data Mining (ICDM), pages 120--129. IEEE, 2014.","DOI":"10.1109\/ICDM.2014.18"},{"key":"key-10.1145\/3041021.3055130-11","doi-asserted-by":"crossref","unstructured":"M. A. F. Gámez, A. C. Gil, and A. J. C. Ruiz. Applying a probabilistic neural network to hotel bankruptcy prediction. Encontros Científicos-Tourism & Management Studies, 12(1):40--52, 2016.","DOI":"10.18089\/tms.2016.12104"},{"key":"key-10.1145\/3041021.3055130-12","unstructured":"P. Georgiev, A. Noulas, and C. Mascolo. Where businesses thrive: Predicting the impact of the olympic games on local retailers through location-based services data. arXiv preprint arXiv:1403.7654, 2014."},{"key":"key-10.1145\/3041021.3055130-13","doi-asserted-by":"crossref","unstructured":"Z. Gu. Analyzing bankruptcy in the restaurant industry: A multiple discriminant model. International Journal of Hospitality Management, 21(1):25--42, 2002.","DOI":"10.1016\/S0278-4319(01)00013-5"},{"key":"key-10.1145\/3041021.3055130-14","doi-asserted-by":"crossref","unstructured":"Z. Gu and L. Gao. A multivariate model for predicting business failures of hospitality firms. Tourism and Hospitality Research, 2(1):37--49, 2000.","DOI":"10.1177\/146735840000200108"},{"key":"key-10.1145\/3041021.3055130-15","doi-asserted-by":"crossref","unstructured":"J. Hertz, A. Krogh, and R. G. Palmer. Introduction to the theory of neural computation, volume 1. Basic Books, 1991.","DOI":"10.1201\/9780429499661-1"},{"key":"key-10.1145\/3041021.3055130-16","doi-asserted-by":"crossref","unstructured":"S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural computation, 9(8):1735--1780, 1997.","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"key-10.1145\/3041021.3055130-17","doi-asserted-by":"crossref","unstructured":"S. S. Jang and Y. Namkung. Perceived quality, emotions, and behavioral intentions: Application of an extended mehrabian--russell model to restaurants. Journal of Business Research, 62(4):451--460, 2009.","DOI":"10.1016\/j.jbusres.2008.01.038"},{"key":"key-10.1145\/3041021.3055130-18","doi-asserted-by":"crossref","unstructured":"P. Jensen. Network-based predictions of retail store commercial categories and optimal locations. Physical Review E, 74(3):035101, 2006.","DOI":"10.1103\/PhysRevE.74.035101"},{"key":"key-10.1145\/3041021.3055130-19","doi-asserted-by":"crossref","unstructured":"D. Karamshuk, A. Noulas, S. Scellato, V. Nicosia, and C. Mascolo. Geo-spotting: Mining online location-based services for optimal retail store placement. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '13, pages 793--801, New York, NY, USA, 2013. ACM.","DOI":"10.1145\/2487575.2487616"},{"key":"key-10.1145\/3041021.3055130-20","doi-asserted-by":"crossref","unstructured":"H. Kim and Z. Gu. A logistic regression analysis for predicting bankruptcy in the hospitality industry. The Journal of Hospitality Financial Management, 14(1):17--34, 2006.","DOI":"10.1080\/10913211.2006.10653812"},{"key":"key-10.1145\/3041021.3055130-21","doi-asserted-by":"crossref","unstructured":"H. Kim and Z. Gu. Predicting restaurant bankruptcy: A logit model in comparison with a discriminant model. Journal of Hospitality & Tourism Research, 30(4):474--493, 2006.","DOI":"10.1177\/1096348006290114"},{"key":"key-10.1145\/3041021.3055130-22","doi-asserted-by":"crossref","unstructured":"S. Y. Kim and A. Upneja. Predicting restaurant financial distress using decision tree and adaboosted decision tree models. Economic Modelling, 36:354--362, 2014.","DOI":"10.1016\/j.econmod.2013.10.005"},{"key":"key-10.1145\/3041021.3055130-23","unstructured":"Q. V. Le and T. Mikolov. Distributed representations of sentences and documents. In Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014, pages 1188--1196, 2014."},{"key":"key-10.1145\/3041021.3055130-24","unstructured":"C. C. Lee. Understanding negative reviews' influence to user reaction in restaurants recommending applications: An experimental study."},{"key":"key-10.1145\/3041021.3055130-25","doi-asserted-by":"crossref","unstructured":"H. Li and J. Sun. Forecasting business failure: The use of nearest-neighbour support vectors and correcting imbalanced samples--evidence from the chinese hotel industry. Tourism Management, 33(3):622--634, 2012.","DOI":"10.1016\/j.tourman.2011.07.004"},{"key":"key-10.1145\/3041021.3055130-26","unstructured":"T. Liu, S. Liu, Z. Chen, and W. Ma. An evaluation on feature selection for text clustering. In Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA, pages 488--495, 2003."},{"key":"key-10.1145\/3041021.3055130-27","unstructured":"T. Mikolov. Recurrent neural network based language model."},{"key":"key-10.1145\/3041021.3055130-28","unstructured":"T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013."},{"key":"key-10.1145\/3041021.3055130-29","unstructured":"T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111--3119, 2013."},{"key":"key-10.1145\/3041021.3055130-30","doi-asserted-by":"crossref","unstructured":"R. J. Oentaryo, E.-P. Lim, D. Lo, F. Zhu, and P. K. Prasetyo. Collective churn prediction in social network. In Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), pages 210--214. IEEE Computer Society, 2012.","DOI":"10.1109\/ASONAM.2012.44"},{"key":"key-10.1145\/3041021.3055130-31","doi-asserted-by":"crossref","unstructured":"M. Olsen, C. Bellas, and L. V. Kish. Improving the prediction of restaurant failure through ratio analysis. International Journal of Hospitality Management, 2(4):187--193, 1983.","DOI":"10.1016\/0278-4319(83)90019-1"},{"key":"key-10.1145\/3041021.3055130-32","doi-asserted-by":"crossref","unstructured":"N. J. Yuan, F. Zhang, D. Lian, K. Zheng, S. Yu, and X. Xie. We know how you live: Exploring the spectrum of urban lifestyles. In Proceedings of the First ACM Conference on Online Social Networks, COSN '13, pages 3--14, New York, NY, USA, 2013. ACM.","DOI":"10.1145\/2512938.2512945"},{"key":"key-10.1145\/3041021.3055130-33","doi-asserted-by":"crossref","unstructured":"F. Zhang, N. J. Yuan, K. Zheng, D. Lian, X. Xie, and Y. Rui. Exploiting dining preference for restaurant recommendation. In Proceedings of the 25th International Conference on World Wide Web, WWW '16, pages 725--735, Republic and Canton of Geneva, Switzerland, 2016. International World Wide Web Conferences Steering Committee.","DOI":"10.1145\/2872427.2882995"},{"key":"key-10.1145\/3041021.3055130-34","doi-asserted-by":"crossref","unstructured":"Y. Zhong, N. J. Yuan, W. Zhong, F. Zhang, and X. Xie. You are where you go: Inferring demographic attributes from location check-ins. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, WSDM '15, pages 295--304, New York, NY, USA, 2015. ACM.","DOI":"10.1145\/2684822.2685287"},{"key":"key-10.1145\/3041021.3055130-35","doi-asserted-by":"crossref","unstructured":"Y. Zhu, E. Zhong, S. J. Pan, X. Wang, M. Zhou, and Q. Yang. Predicting user activity level in social networks. In Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management, CIKM '13, pages 159--168, New York, NY, USA, 2013. ACM.","DOI":"10.1145\/2505515.2505518"}],"event":{"name":"the 26th International Conference","location":"Perth, Australia","acronym":"WWW '17 Companion","number":"26","sponsor":["SIGWEB, ACM Special Interest Group on Hypertext, Hypermedia, and Web","IW3C2, International World Wide Web Conference Committee"],"start":{"date-parts":[[2017,4,3]]},"end":{"date-parts":[[2017,4,7]]}},"container-title":["Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3041021.3055130","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/dl.acm.org\/ft_gateway.cfm?id=3055130&ftid=1865324&dwn=1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T03:03:31Z","timestamp":1750215811000},"score":1,"resource":{"primary":{"URL":"http:\/\/dl.acm.org\/citation.cfm?doid=3041021.3055130"}},"subtitle":[],"proceedings-subject":"World Wide Web Companion","short-title":[],"issued":{"date-parts":[[2017]]},"references-count":35,"URL":"https:\/\/doi.org\/10.1145\/3041021.3055130","relation":{},"subject":[],"published":{"date-parts":[[2017]]}}}