Abstract
Predicting the popularity of web contents in online social networks is essential for many applications. However, existing works are usually under non-incremental settings. In other words, they have to rebuild models from scratch when new data occurs, which are inefficient in big data environments. It leads to an urgent need for incremental prediction, which can update previous results with new data and conduct prediction incrementally. Moreover, the promising direction of group-level popularity prediction has not been well treated, which explores fine-grained information while keeping a low cost. To this end, we identify the problem of incremental group-level popularity prediction, and propose a novel model IGPP to address it. We first predict the group-level popularity incrementally by exploiting the incremental CANDECOMP/PARAFCAC (CP) tensor decomposition algorithm. Then, to reduce the cumulative error by incremental prediction, we propose three strategies to restart the CP decomposition. To the best of our knowledge, this is the first work that identifies and solves the problem of incremental group-level popularity prediction. Extensive experimental results show significant improvements of the IGPP method over other works both in the prediction accuracy and the efficiency.
- Behance. net. 2018. Behance.net Social Network. Retrieved July 31, 2021 from http://www.behance.net/.Google Scholar
- Nuno Moniz and Luis Torgo. 2019. A review on web content popularity prediction: Issues and open challenges. Online Social Networks and Media 12 (2019), 1–20.Google Scholar
Cross Ref
- Brett W. Bader and Tamara G. Kolda. 2015. MATLAB Tensor Toolbox Version 2.6. Retrieved July 31, 2021 from http://www.sandia.gov/~tgkolda/TensorToolbox/.Google Scholar
- Eytan Bakshy, Dean Eckles, Rong Yan, and Itamar Rosenn. 2012. Social influence in social advertising: Evidence from field experiments. In Proc. ACM EC. ACM, New York, NY, 146–161. Google Scholar
Digital Library
- Peng Bao, Hua-Wei Shen, Junming Huang, and Xue Qi Cheng. 2013. Popularity prediction in microblogging network: A case study on Sina Weibo. In Proc. ACM WWW. ACM, New York, NY, 177–178. Google Scholar
Digital Library
- Peng Bao, Hua Wei Shen, Xiaolong Jin, and Xue Qi Cheng. 2015. Modeling and predicting popularity dynamics of microblogs using self-excited Hawkes processes. In Proc. ACM WWW. 9–10. Google Scholar
Digital Library
- Qi Cao, Huawei Shen, Hao Gao, Jinhua Gao, and Xueqi Cheng. 2017. Predicting the popularity of online content with group-specific models. In Proc. ACM WWW. ACM, New York, NY, 765–766. Google Scholar
Digital Library
- Justin Cheng, Lada Adamic, P. Alex Dow, Jon Michael Kleinberg, and Jure Leskovec. 2014. Can cascades be predicted? In Proc. ACM WWW. ACM, New York, NY, 925–936. Google Scholar
Digital Library
- Jinhua Gao, Huawei Shen, Shenghua Liu, and Xueqi Cheng. 2016. Modeling and predicting retweeting dynamics via a mixture process. In Proc. WWW. 33–34. Google Scholar
Digital Library
- Xiaofeng Gao, Zhenhao Cao, Sha Li, Bin Yao, Guihai Chen, and Shaojie Tang. 2019. Taxonomy and evaluation for microblog popularity prediction. ACM Transactions on Knowledge Discovery from Data 13, 2 (2019), Article 15, 40 pages. Google Scholar
Digital Library
- Fei Hao, Shuai Li, Geyong Min, Hee-Cheol Kim, Stephen S. Yau, and Laurence T. Yang. 2015. An efficient approach to generating location-sensitive recommendations in ad-hoc social network environments. IEEE Transactions on Services Computing 8, 3 (2015), 520–533.Google Scholar
Cross Ref
- Fei Hao, Doo-Soon Park, Geyong Min, Young-Sik Jeong, and Jong-Hyuk Park. 2016. k-Cliques mining in dynamic social networks based on triadic formal concept analysis. Neurocomputing 209 (2016), 57–66. Google Scholar
Digital Library
- Johan Håstad. 1990. Tensor rank is NP-complete. Journal of Algorithms 11, 4 (1990), 644–654. Google Scholar
Digital Library
- 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 Proc. ACM WWW. 725–733. Google Scholar
Digital Library
- Chunli Huang, Wenjun Jiang, Jie Wu, and Guojun Wang. 2020. Personalized review recommendation based on users’ aspect sentiment. ACM Transactions on Internet Technology 20, 4 (2020), Article 42, 26 pages. Google Scholar
Digital Library
- Junming Huang, Chao Li, Wen-Qiang Wang, Hua-Wei Shen, Guojie Li, and Xue-Qi Cheng. 2014. Temporal scaling in information propagation. Scientific Reports 4 (2014), 5334.Google Scholar
Cross Ref
- W. Jiang, J. Wu, F. Li, G. Wang, and H. Zheng. 2016. Trust evaluation in online social networks using generalized flow. IEEE Transactions on Computers 65, 3 (2016), 952–963. Google Scholar
Digital Library
- Wenjun Jiang, Jie Wu, Guojun Wang, and Huanyang Zheng. 2014. FluidRating: A time-evolving rating scheme in trust-based recommendation systems using fluid dynamics. In Proc. IEEE INFOCOM. 1707–1715.Google Scholar
Cross Ref
- W. Jiang, J. Wu, G. Wang, and H. Zheng. 2016. Forming opinions via trusted friends: Time-evolving rating prediction using fluid dynamics. IEEE Transactions on Computers 65, 4 (2016), 1211–1224. Google Scholar
Digital Library
- George Karypis and Vipin Kumar. 1998. Multilevel k-way partitioning scheme for irregular graphs. Journal of Parallel and Distributed Computing 48, 1 (1998), 96–129. Google Scholar
Digital Library
- Su-Do Kim, Sung-Hwan Kim, and Hwan-Gue Cho. 2011. Predicting the virtual temperature of web-blog articles as a measurement tool for online popularity. In Proc. IEEE CIT. IEEE, Los Alamitos, CA, 449–454. Google Scholar
Digital Library
- Tamara G. Kolda and Brett W. Bader. 2009. Tensor decompositions and applications. SIAM Review 51, 3 (2009), 455–500. Google Scholar
Digital Library
- Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010. What is Twitter, a social network or a news media? In Proc. ACM WWW. 591–600. Google Scholar
Digital Library
- Kristina Lerman and Tad Hogg. 2010. Using a model of social dynamics to predict popularity of news. In Proc. ACM WWW. 621–630. Google Scholar
Digital Library
- Yuchen Li, Ju Fan, Yanhao Wang, and Kian-Lee Tan. 2018. Influence maximization on social graphs: A survey. IEEE Transactions on Knowledge and Data Engineering 30, 10 (2018), 1852–1872.Google Scholar
Cross Ref
- Yasuko Matsubara, Yasushi Sakurai, Christos Faloutsos, Tomoharu Iwata, and Masatoshi Yoshikawa. 2012. Fast mining and forecasting of complex time-stamped events. In Proc. ACM SIGKDD. ACM, New York, NY, 271–279. Google Scholar
Digital Library
- Zhongchen Miao, Kai Chen, Yi Fang, Jianhua He, Yi Zhou, Wenjun Zhang, and Hongyuan Zha. 2017. Cost-effective online trending topic detection and popularity prediction in microblogging. ACM Transactions on Information Systems 35, 3 (2017), Article 18, 36 pages. Google Scholar
Digital Library
- Zhongchen Miao, Kai Chen, Yi Zhou, Hongyuan Zha, and Wenjun Zhang. 2015. Online trendy topics detection in microblogs with selective user monitoring under cost constraints. In Proc. IEEE ICC.Google Scholar
Cross Ref
- Lukás Neumann, Andrew Zisserman, and Andrea Vedaldi. 2019. Future event prediction: If and when. In Proc. IEEE CVPR. IEEE, Los Alamitos, CA, 2935–2943.Google Scholar
Cross Ref
- Evangelos E. Papalexakis, Christos Faloutsos, and Nicholas D. Sidiropoulos. 2017. Tensors for data mining and data fusion: Models, applications, and scalable algorithms. ACM Transactions on Intelligent Systems and Technology 8, 2 (2017), 16. Google Scholar
Digital Library
- Henrique Pinto, Jussara M. Almeida, and Marcos A. Gonçalves. 2013. Using early view patterns to predict the popularity of YouTube videos. In Proc. ACM WSDM. ACM, New York, NY, 365–374. Google Scholar
Digital Library
- Felix Salfner, Maren Lenk, and Miroslaw Malek. 2010. A survey of online failure prediction methods. ACM Computing Surveys 42, 3 (2010), Article 10, 42 pages. Google Scholar
Digital Library
- Hua Wei Shen, Dashun Wang, Chaoming Song, and Albert-Laszlo Barabasi. 2014. Modeling and predicting popularity dynamics via reinforced poisson processes. In Proc. AAAI. 291–297. Google Scholar
Digital Library
- Gabor Szabo and Bernardo A. Huberman. 2010. Predicting the popularity of online content. Communications of the ACM 53, 8 (2010), 80–88. Google Scholar
Digital Library
- Alexandru Tatar, Panayotis Antoniadis, Marcelo Dias De Amorim, and Serge Fdida. 2012. Ranking news articles based on popularity prediction. In Proc. IEEE/ACM ASONAM. IEEE, Los Alamitos, CA, 106–110. Google Scholar
Digital Library
- Alexandru Tatar, Panayotis Antoniadis, Marcelo Dias De Amorim, and Serge Fdida. 2014. From popularity prediction to ranking online news. Social Network Analysis and Mining 4, 1 (2014), 174.Google Scholar
Cross Ref
- 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 Scholar
Cross Ref
- Alexandru Tatar, Jérémie Leguay, Panayotis Antoniadis, Arnaud Limbourg, Marcelo Dias de Amorim, and Serge Fdida. 2011. Predicting the popularity of online articles based on user comments. In Proc. ACM WIMS. ACM, New York, NY, 67. Google Scholar
Digital Library
- Jingjing Wang, Wenjun Jiang, Kenli Li, and Keqin Li. 2021. Reducing cumulative errors of incremental CP decomposition in dynamic online social networks. ACM Transactions on Knowledge Discovery from Data 15, 3 (2021), Article 1, 33 pages.Google Scholar
- Yanhao Wang, Qi Fan, Yuchen Li, and Kian-Lee Tan. 2017. Real-time influence maximization on dynamic social streams. Proc.eedings of the VLDB Endowment 10, 7 (2017), 805–816. Google Scholar
Digital Library
- Yanhao Wang, Yuchen Li, Ju Fan, and Kian-Lee Tan. 2018. Location-aware influence maximization over dynamic social streams. ACM Transactions on Information Systems 36, 4 (2018), Article 43, 35 pages. Google Scholar
Digital Library
- Peike Xia, Wenjun Jiang, Jie Wu, Surong Xiao, and Guojun Wang. 2021. Exploiting temporal dynamics in product reviews for dynamic sentiment prediction at the aspect level. ACM Transactions on Knowledge Discovery from Data 15, 4 (2021), Article 68, 29 pages. Google Scholar
Digital Library
- 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 Scholar
Cross Ref
- Jie Xu, Mihaela Van Der Schaar, Jiangchuan Liu, and Haitao Li. 2015. Timely video popularity forecasting based on social networks. In Proc. IEEE INFOCOM. IEEE, Los Alamitos, CA, 2308–2316.Google Scholar
Cross Ref
- Peifeng Yin, Ping Luo, Min Wang, and Wang-Chien Lee. 2012. A straw shows which way the wind blows: Ranking potentially popular items from early votes. In Proc. ACM WSDM. ACM, New York, NY, 623–632. Google Scholar
Digital Library
- Linyun Yu, Peng Cui, Fei Wang, Chaoming Song, and Shiqiang Yang. 2015. From micro to macro: Uncovering and predicting information cascading process with behavioral dynamics. In Proc. IEEE ICDM. IEEE, Los Alamitos, CA, 559–568. Google Scholar
Digital Library
- Tauhid Zaman, Emily B. Fox, and Eric T. Bradlow. 2014. A Bayesian approach for predicting the popularity of tweets. Annals of Applied Statistics 8, 3 (2014), 1583–1611.Google Scholar
Cross Ref
- Jifeng Zhang, Wenjun Jiang, Jinrui Zhang, Jie Wu, and Guojun Wang. 2021. Exploring weather data to predict activity attendance in event-based social network: From the organizer’s view. ACM Transactions on the Web 15, 2 (2021), Article 10, 25 pages. Google Scholar
Digital Library
- Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, and Wenwu Zhu. 2018. TIMERS: Error-bounded SVD restart on dynamic networks. In Proc. AAAI. 224–231.Google Scholar
- Liang Zhao. 2020. Event prediction in big data era: A systematic survey. arXiv:2007.09815.Google Scholar
- Fan Zhou, Xovee Xu, Goce Trajcevski, and Kunpeng Zhang. 2020. A survey of information cascade analysis: Models, predictions and recent advances. arXiv:2005.11041. Google Scholar
Digital Library
- Shuo Zhou, Nguyen Xuan Vinh, James Bailey, Yunzhe Jia, and Ian Davidson. 2016. Accelerating online CP decompositions for higher order tensors. In Proc. ACM SIGKDD. 1375–1384. Google Scholar
Digital Library
Index Terms
Incremental Group-Level Popularity Prediction in Online Social Networks
Recommendations
On popularity prediction of videos shared in online social networks
CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge ManagementPopularity prediction, with both technological and economic importance, has been extensively studied for conventional video sharing sites (VSSes), where the videos are mainly found via searching, browsing, or related links. Recent statistics however ...
Popularity prediction in microblogging network: a case study on sina weibo
WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide WebPredicting the popularity of content is important for both the host and users of social media sites. The challenge of this problem comes from the inequality of the popularity of content. Existing methods for popularity prediction are mainly based on the ...
Early prediction of the future popularity of uploaded videos
Highlights- We propose a new approach to early prediction of new video popularity.
- Our ...
AbstractPredicting the popularity of videos on video sharing sites is important for the formulation of online advertising strategies and commercial marketing. The predicted popularity value can help the system decide which videos to recommend ...






Comments