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Improving the Accuracy of the Video Popularity Prediction Models through User Grouping and Video Popularity Classification

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Published:07 February 2020Publication History
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Abstract

This article proposes a novel approach for enhancing the video popularity prediction models. Using the proposed approach, we enhance three popularity prediction techniques that outperform the accuracy of the prior state-of-the-art solutions. The major components of the proposed approach are two novel mechanisms for ”user grouping” and ”content classification.” The user grouping method is an unsupervised clustering approach that divides the users into an adequate number of user groups with similar interests. The content classification approach identifies the classes of videos with similar popularity growth trends. To predict the popularity of the newly-released videos, our proposed popularity prediction model trains its parameters in each user group and its associated video popularity classes. Evaluations are performed through a 5-fold cross validation and on a dataset containing one month video request records of 26,706 users of BBC iPlayer. Using the proposed grouping technique, user groups of similar interest and up to two video popularity classes for each user group were detected. Our analysis shows that the accuracy of the proposed solution outperforms the state-of-the-art, including Szabo-Huberman (SH), Multivariate Linear (ML), and Multivariate linear Radial Basis Functions (MRBF) models by an average of 45%, 33%, and 24%, respectively. Finally, we discuss how various systems in the network and service management domain such as cache deployment, advertising, and video broadcasting technologies benefit from our findings to illustrate the implications.

References

  1. Zlatka Avramova, Sabine Wittevrongel, Herwig Bruneel, and Danny De Vleeschauwer. 2009. Analysis and modeling of video popularity evolution in various online video content systems: Power-law versus exponential decay. In Proceedings of the 1st International Conference on Evolving Internet (INTERNET’09). IEEE, 95--100.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Jesús Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Gutiérrez. 2013. Recommender systems survey. Knowledge-based Systems 46 (2013), 109--132.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Youmna Borghol, Siddharth Mitra, Sebastien Ardon, Niklas Carlsson, Derek Eager, and Anirban Mahanti. 2011. Characterizing and modelling popularity of user-generated videos. Performance Evaluation 68, 11 (2011), 1037--1055.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Meeyoung Cha, Haewoon Kwak, Pablo Rodriguez, Yong-Yeol Ahn, and Sue Moon. 2009. Analyzing the video popularity characteristics of large-scale user generated content systems. IEEE/ACM Transactions on Networking (TON) 17, 5 (2009), 1357--1370.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Xu Cheng, Cameron Dale, and Jiangchuan Liu. 2007. Understanding the characteristics of internet short video sharing: YouTube as a case study. arXiv preprint arXiv:0707.3670 (2007).Google ScholarGoogle Scholar
  6. Xu Cheng, Cameron Dale, and Jiangchuan Liu. 2008. Statistics and social network of youtube videos. In Proceedings of the 16th International Workshop on Quality of Service (IWQoS'08). IEEE, 229--238.Google ScholarGoogle ScholarCross RefCross Ref
  7. Delia Ciullo, Valentina Martina, Michele Garetto, and Emilio Leonardi. 2015. How much can large-scale video-on-demand benefit from users’ cooperation? IEEE/ACM Transactions on Networking (TON) 23, 6 (2015), 1846--1861.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Riley Crane and Didier Sornette. 2008. Robust dynamic classes revealed by measuring the response function of a social system. Proceedings of the National Academy of Sciences 105, 41 (2008), 15649--15653.Google ScholarGoogle ScholarCross RefCross Ref
  9. Riley Crane, Didier Sornette, et al. 2008. Viral, quality, and junk videos on YouTube: Separating content from noise in an information-rich environment. In Proceedings of the AAAI Spring Symposium: Social Information Processing. 18--20.Google ScholarGoogle Scholar
  10. Jeroen Famaey, Tim Wauters, and Filip De Turck. 2011. On the merits of popularity prediction in multimedia content caching. In Proceedings of the 2011 IFIP/IEEE International Symposium on Integrated Network Management (IM). IEEE, 17--24.Google ScholarGoogle ScholarCross RefCross Ref
  11. Flavio Figueiredo. 2013. On the prediction of popularity of trends and hits for user generated videos. In Proceedings of the 6th ACM International Conference on Web Search and Data Mining. ACM, 741--746.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Giulia Fontanini, Marco Bertini, and Alberto Del Bimbo. 2016. Web video popularity prediction using sentiment and content visual features. In Proceedings of the 2016 ACM International Conference on Multimedia Retrieval. ACM, 289--292.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Jerome Friedman, Trevor Hastie, and Robert Tibshirani. 2001. The Elements of Statistical Learning. Vol. 1. Springer series in statistics, Springer, Berlin.Google ScholarGoogle Scholar
  14. Thomas Hayes. 2006. Scalable System and Method for Predicting Hit Music Preferences for an Individual. US Patent App. 11/253,421.Google ScholarGoogle Scholar
  15. Minh X. Hoang, Xuan-Hong Dang, Xiang Wu, Zhenyu Yan, and Ambuj K. Singh. 2017. GPOP: Scalable group-level popularity prediction for online content in social networks. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 725--733.Google ScholarGoogle Scholar
  16. Wei-jen Hsu, Debojyoti Dutta, and Ahmed Helmy. 2012. Structural analysis of user association patterns in university campus wireless lans. IEEE Transactions on Mobile Computing 11, 11 (2012), 1734--1748.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Anil K. Jain. 2010. Data clustering: 50 years beyond K-means. Pattern Recognition Letters 31, 8 (2010), 651--666.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Liping Jing, Michael K. Ng, and Joshua Zhexue Huang. 2007. An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data. IEEE Transactions on Knowledge and Data Engineering 8 (2007), 1026–1041.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Chenyu Li, Jun Liu, and Shuxin Ouyang. 2016. Characterizing and predicting the popularity of online videos. IEEE Access 4 (2016), 1630--1641.Google ScholarGoogle ScholarCross RefCross Ref
  20. Liying Li, Guodong Zhao, and Rick S. Blum. 2018. A survey of caching techniques in cellular networks: Research issues and challenges in content placement and delivery strategies. IEEE Communications Surveys & Tutorials 20, 3 (2018), 1710–1732.Google ScholarGoogle ScholarCross RefCross Ref
  21. Mark Junjie Li, Michael K Ng, Yiu-ming Cheung, and Joshua Zhexue Huang. 2008. Agglomerative fuzzy k-means clustering algorithm with selection of number of clusters. IEEE Transactions on Knowledge and Data Engineering 20, 11 (2008), 1519--1534.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Jiye Liang, Xingwang Zhao, Deyu Li, Fuyuan Cao, and Chuangyin Dang. 2012. Determining the number of clusters using information entropy for mixed data. Pattern Recognition 45, 6 (2012), 2251--2265.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Hai-yong Liao and Michael K. Ng. 2009. Categorical data clustering with automatic selection of cluster number. Fuzzy Information and Engineering 1, 1 (2009), 5--25.Google ScholarGoogle ScholarCross RefCross Ref
  24. Changsha Ma, Zhisheng Yan, and Chang Wen Chen. 2017. LARM: A lifetime aware regression model for predicting youtube video popularity. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 467--476.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Kianoosh Mokhtarian and Hans-Arno Jacobsen. 2017. Flexible caching algorithms for video content distribution networks. IEEE/ACM Transactions on Networking (TON) 25, 2 (2017), 1062--1075.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Joseph Kee-Yin Ng, Victor Chung-Sing Lee, and Chui Ying Hui. 2008. Client-side caching strategies and on-demand broadcast algorithms for real-time information dispatch systems. IEEE Transactions on Broadcasting 54, 1 (2008), 24--35.Google ScholarGoogle ScholarCross RefCross Ref
  27. Amandianeze O. Nwana, Salman Avestimehr, and Tsuhan Chen. 2013. A latent social approach to YouTube popularity prediction. In Proceedings of the 2013 IEEE Global Communications Conference (GLOBECOM). IEEE, 3138--3144.Google ScholarGoogle ScholarCross RefCross Ref
  28. Shuxin Ouyang, Chenyu Li, and Xueming Li. 2016. A peek into the future: Predicting the popularity of online videos. IEEE Access 4 (2016), 3026--3033.Google ScholarGoogle ScholarCross RefCross Ref
  29. Henrique Pinto, Jussara M. Almeida, and Marcos A. Gonçalves. 2013. Using early view patterns to predict the popularity of YouTube videos. In Proceedings of the 6th ACM International Conference on Web Search and Data Mining. ACM, 365--374.Google ScholarGoogle Scholar
  30. Suman Deb Roy, Tao Mei, Wenjun Zeng, and Shipeng Li. 2013. Towards cross-domain learning for social video popularity prediction. IEEE Transactions on Multimedia 15, 6 (2013), 1255--1267.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Bo Shu, Wei Chen, Zhendong Niu, Changmin Zhang, and Xiaotian Jiang. 2013. A novel method for identifying optimal number of clusters with marginal differential entropy. In Proceedings of the International Conference on Web-Age Information Management. Springer, 371--382.Google ScholarGoogle ScholarCross RefCross Ref
  32. Gabor Szabo and Bernardo A. Huberman. 2010. Predicting the popularity of online content. Commun. ACM 53, 8 (2010), 80--88.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Zhiyi Tan, Yanfeng Wang, Ya Zhang, and Jun Zhou. 2016. A novel time series approach for predicting the long-term popularity of online videos. IEEE Transactions on Broadcasting 62, 2 (2016), 436--445.Google ScholarGoogle ScholarCross RefCross Ref
  34. Alexandru Tatar, Marcelo Dias de Amorim, Serge Fdida, and Panayotis Antoniadis. 2014. A survey on predicting the popularity of web content. Journal of Internet Services and Applications 5, 1 (2014), 8.Google ScholarGoogle ScholarCross RefCross Ref
  35. Jiqiang Wu, Yipeng Zhou, Dah Ming Chiu, and Zirong Zhu. 2016. Modeling dynamics of online video popularity. IEEE Transactions on Multimedia 18, 9 (2016), 1882--1895.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Tingyao Wu, Michael Timmers, Danny De Vleeschauwer, and Werner Van Leekwijck. 2010. On the use of reservoir computing in popularity prediction. In Proceedings of the 2010 Second International Conference on Evolving Internet (INTERNET). IEEE, 19--24.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Lexing Xie, Apostol Natsev, Xuming He, John R. Kender, Matthew Hill, and John R. Smith. 2013. Tracking large-scale video remix in real-world events. IEEE Transactions on Multimedia 15, 6 (2013), 1244--1254.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Jie Xu, Mihaela van der Schaar, Jiangchuan Liu, and Haitao Li. 2015. Forecasting popularity of videos using social media. IEEE Journal of Selected Topics in Signal Processing 9, 2 (2015), 330--343.Google ScholarGoogle ScholarCross RefCross Ref
  39. Chengang Zhu, Guang Cheng, and Kun Wang. 2017. Big data analytics for program popularity prediction in broadcast TV industries. IEEE Access 5 (2017), 24593--24601.Google ScholarGoogle ScholarCross RefCross Ref

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