Abstract
User-item interactions in social media provide a rich dataset for wide applications such as viral marketing and recommender systems. Post retweeting behaviors and venue check-in events by users are the most representative. While existing studies predict items’ rise and fall, i.e., tweet popularity and venue closure detection, using hand-crafted features, this paper aims at exploring feature representation learning to improve prediction performance. We target at two essential time-series classification tasks on social media, including Shutdown Risk Prediction (SRP) of venues and Tweet Popularity Prediction (TPP) of posts. We study how feature representation learning of items can benefit both SRP and TPP tasks. The main idea is to learn item embedding vectors as features in item-item graphs constructed from time series of check-in events and retweeting behaviors. The learned features are used together with manually-defined features to enlarge the representation capability. In the TPP task, we also propose a pattern-aware self-exciting point process (PSEISMIC) model to generate time-series features. Experiments conducted on Instagram, Foursquare, and Twitter datasets exhibit promising performance of jointly utilizing learned and extracted features in both tasks. PSEISMIC can also further boost TPP accuracy. The major contribution of this work is three-fold. First, we propose to jointly deal with SRP and TPP under the same framework of feature extraction and learning. Second, we show that feature presentation learning of items can benefit these two prediction tasks with time series data. Third, by incorporating time series patterns, the proposed PSEISMIC further improves the performance of popularity prediction.
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References
Agarwal, D., Chen, B-C, & Elango, P. (2009). Spatio-temporal models for estimating click-through rate. In Proceedings of the 18th international conference on World Wide Web, WWW ’09 (pp. 21–30).
Asur, S., Huberman, B. A., Szabó, G., & Wang, C. (2011). Trends in social media: Persistence and decay. In Proceedings of the fifth international conference on weblogs and social media, Barcelona, Catalonia, Spain, July 17-21, 2011.
Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011). Everyone’s an influencer: quantifying influence on twitter. In Proceedings of the fourth ACM international conference on web search and data mining (pp. 65–74): ACM.
Bao, P., Shen, H-W, Huang, J., & Cheng, X-Q. (2013). Popularity prediction in microblogging network: A case study on sina weibo. In Proceedings of the 22nd international conference on World Wide Web, WWW ’13 Companion (pp. 177–178).
Barabasi, A-L. (2005). The origin of bursts and heavy tails in human dynamics. Nature, 435(7039), 207.
Blondel, V. D., Guillaume, J-L, Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10), P10008.
Bottou, L. (1991). Stochastic gradient learning in neural networks. Proceedings of Neuro-Nımes, 91(8).
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Chang, B., Jang, G., Kim, S., & Kang, J. (2020). Learning graph-based geographical latent representation for point-of-interest recommendation. In Proceedings of the 29th ACM international conference on information & knowledge management, CIKM ’20 (pp. 135–144).
Chen, H., Chen, Z., Arefin, M. S., & Morimoto, Y. (2012). Place recommendation from check-in spots on location-based online social networks. In 2012 Third international conference on networking and computing (ICNC) (pp. 143–148): IEEE.
Chen, Y., Long, C., Cong, G., & Li, C. (2020). Context-aware deep model for joint mobility and time prediction. In Proceedings of the 13th international conference on web search and data Mining, WSDM ’20 (pp. 106–114).
Cheng, J., Adamic, L., Dow, P. A., Kleinberg, J. M., & Leskovec, J. (2014). Can cascades be predicted?. In Proceedings of the 23rd international conference on World Wide Web, WWW ’14 (pp. 925–936).
Crane, R., & Sornette, D. (2008). Robust dynamic classes revealed by measuring the response function of a social system. Proceedings of the National Academy of Sciences, 105(41), 15649–15653.
Daneshmand, H., Gomez-Rodriguez, M., Song, L., & Schoelkopf, B. (2014). Estimating diffusion network structures: Recovery conditions, sample complexity & soft-thresholding algorithm. In International conference on machine learning (pp. 793–801).
D’Silva, K., Jayarajah, K., Noulas, A., Mascolo, C., & Misra, A. (2018). The role of urban mobility in retail business survival. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2(3), 100:1–100:22.
Du, N., Song, L., Yuan, M., & Smola, A. J. (2012). Learning networks of heterogeneous influence. In Advances in neural information processing systems (pp. 2780–2788).
Gomes, J. B., Phua, C., & Krishnaswamy, S. (2013). Where will you go? mobile data mining for next place prediction. In International conference on data warehousing and knowledge discovery (pp. 146–158): Springer.
Gomez-Rodriguez, M., Leskovec, J., & Schölkopf, B. (2013). Modeling information propagation with survival theory. In International conference on machine learning (pp. 666–674).
Grover, A., & Leskovec, J. (2016). Node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’16 (pp. 855–864).
Parsa, H. G., Smith, S. R., Parsa, R. A., Bujisic, M., & van derRest, J.-P.I. (2015). Why restaurants fail? part iv: The relationship between restaurant failures and demographic factors. Cornell Hospitality Quarterly, 56(1), 80–90.
Hsieh, H-P, Li, C-T, & Lin, S-D. (2015). Estimating potential customers anywhere and anytime based on location-based social networks. In Proceedings of joint european conference on machine learning and knowledge discovery in databases (pp. 576–592).
Karamshuk, D., Noulas, A., Scellato, S., Nicosia, V., & Mascolo, C. (2013). 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 (pp. 793–801).
Kovalcinova, L., & Polacek, M. (2015). Yelp data-set challenge part 6: Predicting whether business is open or closed and suggesting the good business practices. https://rpubs.com/Kvitnuca_Zahradka/139697.
Li, C.-T., Shan, M.-K., Jheng, S.-H., & Chou, K.-C. (2016). Exploiting concept drift to predict popularity of social multimedia in microblogs. Information Sciences, 339, 310–331.
Lian, D., Wu, Y., Ge, Y., Xie, X., & Chen, E. (2020). Geography-aware sequential location recommendation. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, KDD ’20 (pp. 2009–2019).
Liu, Y., Guo, B., Li, N., Zhang, J., Chen, J., Zhang, D., Liu, Y., Yu, Z., Zhang, S., & Yao, L. (2019). Deepstore: An interaction-aware wide deep model for store site recommendation with attentional spatial embeddings. IEEE Internet of Things Journal, 6(4), 7319–7333.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv:1301.3781.
Noulas, A., Scellato, S., Lathia, N., & Mascolo, C. (2012). Mining user mobility features for next place prediction in location-based services. In Data mining (ICDM), 2012 IEEE 12th international conference on (pp. 1038–1043): IEEE.
Perozzi, B., Al-Rfou, R., & Skiena, S. (2014). Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 701–710): ACM.
Rodriguez, M. G., Leskovec, J., Balduzzi, D., & Schölkopf, B. (2014). Uncovering the structure and temporal dynamics of information propagation. Network Science, 2(1), 26–65.
Shulman, B., Sharma, A., & Cosley, D. (2016). Predictability of popularity: Gaps between prediction and understanding. In Proceedings of the tenth international conference on web and social media, Cologne, Germany, May 17-20, 2016. (pp. 348–357).
Subbian, K., Prakash, B. A., & Adamic, L. (2017). Detecting large reshare cascades in social networks. In Proceedings of the 26th international conference on World Wide Web, WWW ’17 (pp. 597–605).
Wang, L., Gopal, R., Shankar, R., & Pancras, J. (2015). On the brink: Predicting business failure with mobile location-based checkins. Decision Support Systems, 76(C), 3–13.
Wang, F., Chen, L., & Pan, W. (2016). Where to place your next restaurant?: Optimal restaurant placement via leveraging user-generated reviews. In Proceedings of the 25th ACM international on conference on information and knowledge management, CIKM ’16 (pp. 2371–2376).
Wang, Q., Zhang, J., Guo, B., Hao, Z., Zhou, Y., Sun, J., Yu, Z., & Zheng, Y. (2019). Cityguard: Citywide fire risk forecasting using a machine learning approach. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 3(4).
Weng, L., Menczer, F., & Ahn, Y-Y. (2014). Predicting successful memes using network and community structure. In Proceedings of the eighth international conference on weblogs and social media, ICWSM (pp. 535–544).
Yang, D., Qu, B., Yang, J., & Cudre-Mauroux, P. (2019). Revisiting user mobility and social relationships in lbsns: A hypergraph embedding approach. In The World Wide Web Conference, WWW ’19 (pp. 2147–2157).
Zaman, T., Fox, E. B., Bradlow, E. T., & et al. (2014). A bayesian approach for predicting the popularity of tweets. The Annals of Applied Statistics, 8(3), 1583–1611.
Zhang, Y., Li, B., & Hong, J. (2016). Understanding user economic behavior in the city using large-scale geotagged and crowdsourced data. In Proceedings of the 25th international conference on World Wide Web, WWW ’16 (pp. 205–214).
Zhao, Q., Erdogdu, M. A., He, H. Y., Rajaraman, A., & Leskovec, J. (2015). SEISMIC: A self-exciting point process model for predicting tweet popularity. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data Mining, Sydney, NSW, Australia, August 10-13, 2015 (pp. 1513–1522).
Zhao, P., Luo, A., Liu, Y., Zhuang, F., Xu, J., Li, Z., Sheng, V. S., & Zhou, X. (2020). Where to go next: A spatio-temporal gated network for next poi recommendation. IEEE Transactions on Knowledge and Data Engineering.
Zhao, J., Du, B., Sun, L., Lv, W., Liu, Y., & Xiong, H. (2021). Deep multi-task learning with relational attention for business success prediction. Pattern Recognition, 110, 107469.
Zhou, K., Zha, H., & Song, L. (2013). Learning social infectivity in sparse low-rank networks using multi-dimensional hawkes processes. In Artificial Intelligence and Statistics (pp. 641–649).
Zhou, X., Mascolo, C., & Zhao, Z. (2019). Topic-enhanced memory networks for personalised point-of-interest recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’19 (pp. 3018–3028).
Acknowledgments
This work is supported by the Ministry of Science and Technology (MOST) of Taiwan under grants 109-2636-E-006-017 (MOST Young Scholar Fellowship) and 109-2221-E-006-173, and also by Academia Sinica under grant AS-TP-107-M05.
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Li, CT., Chen, HY. & Zhang, Y. On exploring feature representation learning of items to forecast their rise and fall in social media. J Intell Inf Syst 56, 409–433 (2021). https://doi.org/10.1007/s10844-020-00632-7
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DOI: https://doi.org/10.1007/s10844-020-00632-7