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Erschienen in: Journal of Intelligent Information Systems 3/2021

07.01.2021

On exploring feature representation learning of items to forecast their rise and fall in social media

verfasst von: Cheng-Te Li, Hsin-Yu Chen, Yang Zhang

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 3/2021

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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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Literatur
Zurück zum Zitat 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). 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).
Zurück zum Zitat 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. 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.
Zurück zum Zitat 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. 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.
Zurück zum Zitat 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). 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).
Zurück zum Zitat Barabasi, A-L. (2005). The origin of bursts and heavy tails in human dynamics. Nature, 435(7039), 207.CrossRef Barabasi, A-L. (2005). The origin of bursts and heavy tails in human dynamics. Nature, 435(7039), 207.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat Bottou, L. (1991). Stochastic gradient learning in neural networks. Proceedings of Neuro-Nımes, 91(8). Bottou, L. (1991). Stochastic gradient learning in neural networks. Proceedings of Neuro-Nımes, 91(8).
Zurück zum Zitat 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). 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).
Zurück zum Zitat 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, 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.
Zurück zum Zitat 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). 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).
Zurück zum Zitat 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). 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).
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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). 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).
Zurück zum Zitat 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. 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.
Zurück zum Zitat 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). 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).
Zurück zum Zitat 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. 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.
Zurück zum Zitat Gomez-Rodriguez, M., Leskovec, J., & Schölkopf, B. (2013). Modeling information propagation with survival theory. In International conference on machine learning (pp. 666–674). Gomez-Rodriguez, M., Leskovec, J., & Schölkopf, B. (2013). Modeling information propagation with survival theory. In International conference on machine learning (pp. 666–674).
Zurück zum Zitat 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). 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).
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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). 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).
Zurück zum Zitat 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). 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).
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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). 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).
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv:1301.3781. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv:1301.​3781.
Zurück zum Zitat 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. 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.
Zurück zum Zitat 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. 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.
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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). 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).
Zurück zum Zitat 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). 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).
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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, 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).
Zurück zum Zitat 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). 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).
Zurück zum Zitat 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). 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).
Zurück zum Zitat 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). 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).
Zurück zum Zitat 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.MathSciNetCrossRef 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.MathSciNetCrossRef
Zurück zum Zitat 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). 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).
Zurück zum Zitat 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, 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).
Zurück zum Zitat 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, 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.
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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, 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).
Zurück zum Zitat 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). 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).
Metadaten
Titel
On exploring feature representation learning of items to forecast their rise and fall in social media
verfasst von
Cheng-Te Li
Hsin-Yu Chen
Yang Zhang
Publikationsdatum
07.01.2021
Verlag
Springer US
Erschienen in
Journal of Intelligent Information Systems / Ausgabe 3/2021
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI
https://doi.org/10.1007/s10844-020-00632-7

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