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Exploring Weather Data to Predict Activity Attendance in Event-based Social Network: From the Organizer’s View

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Published:22 April 2021Publication History
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Abstract

Event-based social networks (EBSNs) connect online and offline lives. They allow online users with similar interests to get together in real life. Attendance prediction for activities in EBSNs has attracted a lot of attention and several factors have been studied. However, the prediction accuracy is not very good for some special activities, such as outdoor activities. Moreover, a very important factor, the weather, has not been well exploited. In this work, we strive to understand how the weather factor impacts activity attendance, and we explore it to improve attendance prediction from the organizer’s view. First, we classify activities into two categories: the outdoor and the indoor activities. We study the different ways that weather factors may impact these two kinds of activities. We also introduce a new factor of event duration. By integrating the above factors with user interest and user-event distance, we build a model of attendance prediction with the weather named GBT-W, based on the Gradient Boosting Tree. Furthermore, we develop a platform to help event organizers estimate the possible number of activity attendance with different settings (e.g., different weather, location) to effectively plan their events. We conduct extensive experiments, and the results show that our method has a better prediction performance on both the outdoor and the indoor activities, which validates the reasonability of considering weather and duration.

References

  1. [n.d.]. Retrieved from: https://www.douban.com/.Google ScholarGoogle Scholar
  2. [n.d.]. Retrieved from: https://www.meetup.com/.Google ScholarGoogle Scholar
  3. [n.d.]. Retrieved from: http://www.plancast.co.uk/.Google ScholarGoogle Scholar
  4. [n.d.]. Retrieved from: http://tianqi.2345.com/.Google ScholarGoogle Scholar
  5. A. Spence, W. Poortinga, and N. Pidgeon. 2012. The psychological distance of climate change. Risk Anal. 32, 6 (2012), 957--972.Google ScholarGoogle ScholarCross RefCross Ref
  6. Mohammad Aliannejadi and Fabio Crestani. 2018. Personalized context-aware point of interest recommendation. ACM Trans. Inf. Syst. 36, 4 (Oct. 2018). DOI:https://doi.org/10.1145/3231933 Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Jing Chen and Wenjun Jiang. 2019. Context-aware personalized POI sequence recommendation. In Proceedings of the Conference on Smart City and Informatization. 197--210. DOI:https://doi.org/10.1007/978-981-15-1301-5_16Google ScholarGoogle ScholarCross RefCross Ref
  8. Yunqi Dong and Wenjun Jiang. 2019. Brand purchase prediction based on time-evolving user behaviors in e-commerce. Concurr. Comput.: Pract. Exper. 31, 1 (2019). DOI:https://doi.org/10.1002/cpe.4882Google ScholarGoogle Scholar
  9. Rong Du, Zhiwen Yu, Tao Mei, Zhitao Wang, Zhu Wang, and Bin Guo. 2014. Predicting activity attendance in event-based social networks: Content, context and social influence. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. 425--434. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Kaiyu Feng, Gao Cong, Sourav S. Bhowmick, and Shuai Ma. 2014. In search of influential event organizers in online social networks. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 63--74. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Jerome H. Friedman. 2002. Stochastic gradient boosting. Comput. Statist. Data Anal. 38, 4 (2002), 367--378. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. T. Fu, C. Wang, and N. Cheng. 2020. Deep learning based joint optimization of renewable energy storage and routing in vehicular energy network. IEEE Internet Things J. (2020), 1--1. DOI:https://doi.org/10.1109/JIOT.2020.2966660Google ScholarGoogle Scholar
  13. Petko Georgiev, Anastasios Noulas, and Cecilia Mascolo. 2014. The call of the crowd: Event participation in location-based social services. Proceedings of the International AAAI Conference on Web and Social Media 8, 1 (2014).Google ScholarGoogle Scholar
  14. Long Guo, Dongxiang Zhang, Yuan Wang, Huayu Wu, Bin Cui, and Kian-Lee Tan. 2018. CO 2: Inferring personal interests from raw footprints by connecting the offline world with the online world. ACM Trans. Inf. Syst. 36 (03 2018), 1--29. DOI:https://doi.org/10.1145/3182164 Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Junwei Han, Jianwei Niu, Alvin Chin, Wei Wang, Chao Tong, and Xia Wang. 2012. How online social network affects offline events: A case study on Douban. In Proceedings of the International Conference on Ubiquitous Intelligence and Computing and International Conference on Autonomic and Trusted Computing. 752--757. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Chunli Huang, Wenjun Jiang, Jie Wu, and Guojun Wang. 2020. Personalized review recommendation based on users’ aspect sentiment. ACM Trans. Internet Technol. 20, 4 (Oct. 2020). DOI:https://doi.org/10.1145/3414841 Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. J. H. G. Jeuring and K. B. M. Peters. 2013. The influence of the weather on tourist experiences: Analysing travel blog narratives. J. Vacat. Market. 19, 3 (2013), 209--219.Google ScholarGoogle ScholarCross RefCross Ref
  18. Jyun-Yu Jiang and Cheng-Te Li. 2016. Analyzing social event participants for a single organizer. In Proceedings of the 10th International AAAI Conference on Web and Social Media.Google ScholarGoogle Scholar
  19. Wenjun Jiang, Guojun Wang, Md Zakirul Alam Bhuiyan, and Jie Wu. 2016. Understanding graph-based trust evaluation in online social networks: Methodologies and challenges. ACM Comput. Surv. 49, 1 (May 2016). DOI:https://doi.org/10.1145/2906151 Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. W. Jiang, J. Wu, F. Li, G. Wang, and H. Zheng. 2016. Trust evaluation in online social networks using generalized flow. IEEE Trans. Comput. 65, 3 (2016), 952--963. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. W. Jiang, J. Wu, G. Wang, and H. Zheng. 2016. Forming opinions via trusted friends: Time-evolving rating prediction using fluid dynamics. IEEE Trans. Comput. 65, 4 (2016), 1211--1224. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Yaron Kanza, Elad Kravi, Eliyahu Safra, and Yehoshua Sagiv. 2017. Location-based distance measures for geosocial similarity. ACM Trans. Web 11, 3 (July 2017). DOI:https://doi.org/10.1145/3054951 Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Akanksha Kumari, Ashish Kumar Singh, and Nagamma Patil. 2017. Travel recommendation system using geotagged photos. In Proceedings of the 7th International Conference on Computer and Communication Technology (ICCCT’17). ACM, New York, NY, 22--26. DOI:http://doi.acm.org/10.1145/3154979.3154995 Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Jiwei Li, Xun Wang, and Eduard Hovy. 2014. What a nasty day: Exploring mood-weather relationship from Twitter. In Proceedings of the ACM International Conference on Conference on Information and Knowledge Management. 1309--1318. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Peng Liu, Yue Ding, and Tingting Fu. 2019. Optimal ThrowBoxes assignment for big data multicast in VDTNs. Wirel. Netw. (2019), 1--11. DOI:10.1007/s11276-019-01974-zGoogle ScholarGoogle ScholarDigital LibraryDigital Library
  26. Xiang Liu and Torsten Suel. 2017. What makes a group fail: Modeling social group behavior in event-based social networks. In Proceedings of the IEEE International Conference on Big Data. 951--956.Google ScholarGoogle Scholar
  27. Xingjie Liu, Yuanyuan Tian, Yuanyuan Tian, Wang Chien Lee, John Mcpherson, and Jiawei Han. 2012. Event-based social networks: Linking the online and offline social worlds. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1032--1040. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Yao Lu, Zhi Qiao, Chuan Zhou, Yue Hu, and Li Guo. 2016. Location-aware friend recommendation in event-based social networks: A Bayesian latent factor approach. In Proceedings of the ACM International Conference on Information and Knowledge Management. 1957--1960. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Augusto Q. Macedo, Leandro B. Marinho, and Rodrygo L. T. Santos. 2015. Context-aware event recommendation in event-based social networks. In Proceedings of the ACM Conference on Recommender Systems. 123--130. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Suvojit Manna, Sanket Biswas, Riyanka Kundu, Somnath Rakshit, Priti Gupta, and Subhas Barman. 2018. A statistical approach to predict flight delay using gradient boosted decision tree. In Proceedings of the International Conference on Computational Intelligence in Data Science. 1--5.Google ScholarGoogle Scholar
  31. Tunde J. Ogundele, Chi Yin Chow, and Jia Dong Zhang. 2017. EventRec: Personalized event recommendations for smart event-based social networks. In Proceedings of the IEEE International Conference on Smart Computing. 1--8.Google ScholarGoogle ScholarCross RefCross Ref
  32. Wei-Jian Qin. 2003. Analysis on weather comfort for traveling in Guangxi. J. Guangxi Meteorol. 4 (2003).Google ScholarGoogle Scholar
  33. Jieying She, Yongxin Tong, and Lei Chen. 2015. Utility-aware social event-participant planning. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. 1629--1643. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Yangkai Shi and Wenjun Jiang. 2017. Point-of-interest recommendations: Capturing the geographical influence from local trajectories. In Proceedings of the IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA’17).Google ScholarGoogle ScholarCross RefCross Ref
  35. Jason Shuo Zhang and Qin Lv. 2019. Understanding event organization at scale in event-based social networks. ACM Trans. Intell. Syst. Technol. 10 (01 2019), 1--23. DOI:https://doi.org/10.1145/3243227 Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Wenting Tu, David W. Cheung, Nikos Mamoulis, Min Yang, and Ziyu Lu. 2017. Activity recommendation with partners. ACM Trans. Web 12, 1 (Sept. 2017). DOI:http://doi.acm.org/10.1145/3121407 Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Senzhang Wang, Xiaoming Zhang, Jianping Cao, Lifang He, Leon Stenneth, Philip S. Yu, Zhoujun Li, and Zhiqiu Huang. 2017. Computing urban traffic congestions by incorporating sparse GPS probe data and social media data. ACM Trans. Inf. Syst. 35, 4 (July 2017). DOI:https://doi.org/10.1145/3057281 Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Zhibo Wang, Yongquan Zhang, Yijie Li, Qian Wang, and Feng Xia. 2017. Exploiting social influence for context-aware event recommendation in event-based social networks. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’17). 1--9.Google ScholarGoogle ScholarCross RefCross Ref
  39. Xian Wu, Yuxiao Dong, Baoxu Shi, Ananthram Swami, and Nitesh V. Chawla. 2018. Who will attend this event together? Event attendance prediction via deep LSTM networks. In Proceedings of the SIAM International Conference on Data Mining. SIAM, 180--188.Google ScholarGoogle Scholar
  40. Bin Xu, Alvin Chin, and Cosley Dan. 2013. On how event size and interactivity affect social networks. In Proceedings of the CHI ’13 Extended Abstracts on Human Factors in Computing Systems. 865--870. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Y. Xu, J. Ren, G. Wang, C. Zhang, J. Yang, and Y. Zhang. 2019. A blockchain-based nonrepudiation network computing service scheme for industrial IoT. IEEE Trans. Indust. Inform. 15, 6 (June 2019), 3632--3641. DOI:https://doi.org/10.1109/TII.2019.2897133Google ScholarGoogle Scholar
  42. Y. Xu, J. Ren, Y. Zhang, C. Zhang, B. Shen, and Y. Zhang. 2019. Blockchain empowered arbitrable data auditing scheme for network storage as a service. IEEE Trans. Serv. Comput. (2019), 1--1. DOI:https://doi.org/10.1109/TSC.2019.2953033Google ScholarGoogle Scholar
  43. Y. Trope and N. Liberman. 2010. Construal-level theory of psychological distance. Psychol. Rev. 117, 2 (2010), 440--463.Google ScholarGoogle Scholar
  44. Jerry Ye, Jyh Herng Chow, Jiang Chen, and Zhaohui Zheng. 2009. Stochastic gradient boosted distributed decision trees. In Proceedings of the ACM Conference on Information and Knowledge Management. 2061--2064. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Hongzhi Yin, Bin Cui, Ling Chen, Zhiting Hu, and Chengqi Zhang. 2015. Modeling location-based user rating profiles for personalized recommendation. ACM Trans. Knowl. Discov. Data 9, 3 (April 2015). DOI:https://doi.org/10.1145/2663356 Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Hongzhi Yin, Bin Cui, Yizhou Sun, Zhiting Hu, and Ling Chen. 2014. LCARS: A spatial item recommender system. ACM Trans. Inf. Syst. 32, 3 (July 2014). DOI:https://doi.org/10.1145/2629461 Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Zhiwen Yu, Rong Du, Bin Guo, Huang Xu, Tao Gu, Zhu Wang, and Daqing Zhang. 2015. Who should I invite for my party?: Combining user preference and influence maximization for social events. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. 879--883. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Chongsheng Zhang, Changchang Liu, Xiangliang Zhang, and George Almpanidis. 2017. An up-to-date comparison of state-of-the-art classification algorithms. Exp. Syst. Applic. 82, C (2017), 128--150. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Shuo Zhang, Khaled Alanezi, Mike Gartrell, Richard Han, Qin Lv, and Shivakant Mishra. 2018. Understanding group event scheduling via the OutWithFriendz mobile application. Proc. ACM Interact., Mob., Wear. Ubiq. Technol. 1, 4 (2018), 175. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Shuo Zhang and Qin Lv. 2018. Hybrid EGU-based group event participation prediction in event-based social networks. Knowl.-based Syst. 143 (2018), 19--29.Google ScholarGoogle Scholar
  51. S. Zhang, G. Wang, M. Z. A. Bhuiyan, and Q. Liu. 2018. A dual privacy preserving scheme in continuous location-based services. IEEE Internet Things J. 5, 5 (2018), 4191--4200. DOI:https://doi.org/10.1109/JIOT.2018.2842470Google ScholarGoogle ScholarCross RefCross Ref
  52. Xiaomei Zhang, Jing Zhao, and Guohong Cao. 2015. Who will attend?—Predicting event attendance in event-based social network. In Proceedings of the IEEE International Conference on Mobile Data Management. 74--83. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Yinuo Zhang, Hao Wu, Anand Panangadan, and Viktor K. Prasanna. 2015. Integration of heterogeneous web services for event-based social networks. In Proceedings of the IEEE International Conference on Information Reuse and Integration. 57--63. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Xu Zhou, Kenli Li, Guoqing Xiao, Yantao Zhou, and Keqin Li. 2016. Top k favorite probabilistic products queries. IEEE Trans. Knowl. Data Eng. 28, 10 (2016), 2808--2821. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Xu Zhou, Kenli Li, Zhibang Yang, and Keqin Li. 2018. Finding optimal skyline product combinations under price promotion. IEEE Trans. Knowl. Data Eng. 31, 1 (2018), 138--151.Google ScholarGoogle ScholarDigital LibraryDigital Library

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