Skip to main content
SpringerLink
Log in

A layer-wise deep stacking model for social image popularity prediction

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

In this paper, we present a Layer-wise Deep Stacking (LDS) model to predict the popularity of Flickr-like social posts. LDS stacks multiple regression models in multiple layers, which enables the different models to complement and reinforce each other. To avoid overfitting, a dropout module is introduced to randomly activate the data being fed into the regression models in each layer. In particular, a detector is devised to determine the depth of LDS automatically by monitoring the performance of the features achieved by the LDS layers. Extensive experiments conducted on a public dataset consisting of 432K Flickr image posts manifest the effectiveness and significance of the LDS model and its components. LDS achieves competitive performance on multiple metrics: Spearman’s Rho: 83.50%, MAE: 1.038, and MSE: 2.011, outperforming state-of-the-art approaches for social image popularity prediction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8

Similar content being viewed by others

References

  1. Aloufi, S., Zhu, S., El, S.A.: On the prediction of flickr image popularity by analyzing heterogeneous social sensory data. Sensors 17(3), 631 (2017)

    Article  Google Scholar 

  2. Ansari, A., Essegaier, S., Kohli, R.: Internet recommendation systems. J. Mark. Res. 37(3), 363–375 (2000)

    Article  Google Scholar 

  3. Asur, S., Huberman, B.A.: Predicting the future with social media. In: IEEE, vol. 1, pp 492–499 (2010)

  4. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2010)

    Article  MATH  Google Scholar 

  5. Cao, D., Ji, R., Lin, D., Li, S.: A cross-media public sentiment analysis system for microblog. Multimedia Systems, Springer 22(4), 479–486 (2016)

    Article  Google Scholar 

  6. Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785–794. ACM (2016)

  7. Chollet, F.: Xception: deep learning with depthwise separable convolutions. arXiv:161002357 (2017)

  8. Cortes, C., Vapnik, V.: Support-vector networks[J]. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  9. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.F.: Imagenet: a large-scale hierarchical image database. In: Computer vision and pattern recognition, CVPR 2009, 0pp. 248–255 (2009)

  10. Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)

    Article  MATH  Google Scholar 

  11. Guo, C., Li, B., Tian, X.: Flickr group recommendation using rich social media information. Neurocomputing 204, 8–16 (2016)

    Article  Google Scholar 

  12. Hsu, C.C., Lee, Y.C., Lu, P.E., Lu, S.S., Lai, H.T.: Social media prediction based on residual learning and random forest. In: Proceedings of the 2017 ACM on multimedia conference, pp. 1865–1870 (2017)

  13. Huang, X., Gao, Y., Quan, F., Sang, J., Xu, C.: Towards SMP challenge: stacking of diverse models for social image popularity prediction. In: Proceedings of the 2017 ACM on multimedia conference, pp. 1895–1900 (2017)

  14. Jahrer, M., Töscher, A., Legenstein, R.: Combining predictions for accurate recommender systems. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 693–702. ACM (2010)

  15. Khosla, A., Das, S.A., Hamid, R.: What makes an image popular?. In: Proceedings of the 23rd international conference on World Wide Web, pp. 867–876. ACM (2014)

  16. Lee, W.Y., Kuo, Y., Hsieh, P.J., Cheng, W., Chao, T., Hsieh, H.L., Tsai, C.E., Chang, H., Lan, J., Hsu, W.: Unsupervised latent aspect discovery for diverse event summarization. In: Proceedings of the 23rd ACM international conference on Multimedia, pp. 197–200. ACM (2015)

  17. Li, C., Yue, L., Mei, Q., Wang, D., Sandeep, P.: Sandeep Pandey Click-through Prediction for Advertising in Twitter Timeline. In: ACM SIGKDD, pp. 1959–1968 (2015)

  18. Li, L.S.R., Gao, J., Yang, Z., Liu, W.: A hybrid model combining convolutional neural network with XGBoost for predicting social media popularity. In: Proceedings of the 2017 ACM on multimedia conference, pp. 1912–1917 (2017)

  19. Liu, B.: Sentiment analysis and opinion mining. Encyclopedia of Machine Learning and Data Mining, pp. 1–10 (2016)

  20. Lv, J., Liu, W., Zhang, M., Gong, H., Wu, B., Ma, H.: Multi-feature fusion for predicting social media popularity. In: Proceedings of the 2017 ACM on multimedia conference, pp. 1883–1888 (2017)

  21. Natarajan, P., Wu, S., Vitaladevuni, S., Zhuang, X.: Multimodal feature fusion for robust event detection in Web videos. In: Computer vision and pattern recognition IEEE, pp. 1298–1305 (2012)

  22. Nguyen, H.M., Woo, S., Im, J., Jun, T., Kim, D.: A Workload Prediction Approach Using Models Stacking Based on Recurrent Neural Network and Autoencoder IEEE, 929-936 (2016)

  23. Park, T., Casella, G.: The bayesian lasso. J. Am. Stat. Assoc. 103(482), 681–686 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  24. Rabinovich, M., Spatschek, O.: Web caching and replication. SIGMOD (2003)

  25. Roy, S.D., Mei, T., Zeng, W., L, S.: Towards cross-domain learning for social video popularity prediction. IEEE Transactions on multimedia 15, 1255–1267 (2013)

    Article  Google Scholar 

  26. Schinas, M., Papadopoulos, S., Petkos, G., Kompatsiaris, Y., Mitkas, P.A.: Multimodal graph-based event detection and summarization in social media streams. In: Proceedings of the 23rd ACM international conference on Multimedia, pp. 189-192. ACM (2015)

  27. Sill, J., Takcs, G., Mackey, L., Lin, D.: Feature-weighted linear stacking. arXiv:09110460 (2009)

  28. Simon, K.: Digital in 2017. https://wearesocial.com/special-reports/digital-in-2017-global-overview (2017)

  29. Snedecor, G.W., Cocheran, W.G.: Statistical methods, 7th edn., p 192. Iowa State University Press, Ames (1980)

    Google Scholar 

  30. Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Advances in neural information processing systems, pp 2951–2959 (2012)

  31. Spyrou, E., Mylonas, P.: Analyzing Flickr metadata to extract location-based information and semantically organize its photo content. Neurocomputing 172, 114–133 (2016)

    Article  Google Scholar 

  32. Srivastava, N., Hinton, G., Krizhevsky, A.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  33. Tatar, A., de Amorim, M.D., Fdida, S., Antoniadis, P.: A Survey on Predicting the Popularity of Web Content, vol. 5, p 8 (2014)

  34. Tkachenko, N., Jarvis, S., Procter, R.: Predicting floods with flickr tags. https://doi.org/10.1371/journal.pone.0172870/. Accessed 24 Feb. 2017 (2017)

  35. Vishwanath, D., Gupta, S.: Adding CNNs to the Mix: stacking models for sentiment classification. In: India conference (INDICON), 2016 IEEE Annual, pp 1–4 (2016)

  36. Wang, P., Wang, Z., Wang, D.: Recurrent deep stacking networks for speech recognition. arXiv:161204675 (2016)

  37. Wang, s, Guo, W.: Sparse multi-graph embedding for multimodal feature representation. IEEE Trans. Multimedia PP 99, 1–1 (2017)

    Google Scholar 

  38. Wang, W., Zhang, W.: Combining multiple features for image popularity prediction in social media. In: Proceedings of the 2017 ACM on multimedia conference, pp. 1865–1870. ACM (2017)

  39. Wu, B., Cheng, W.H., Zhang, Y., Huang, Q., Li, J., Mei, T.: Sequential prediction of social media popularity with deep temporal context networks. In: Proceedings of the Twenty-Sixth international joint conference on artificial intelligence, IJCAI (2017)

  40. Wu, B., Mei, T., Cheng, W.H., Zhang, Y.: Time matters: multi-scale temporalization of social media popularity. In: Proceedings of the 2016 ACM on multimedia conference, pp. 1336–1344 (2016)

  41. Wu, B., Mei, T., Cheng, W.H., Zhang, Y.: Unfolding temporal dynamics: predicting social media popularity using multi-scale temporal decomposition Proceeding of AAAI, pp. 272–278 (2016)

  42. Wu, B., Mei, T., Cheng, W.H., Zhang, Y.: https://social-media-prediction.githubprediction.github.io/MM17PredictionChallenge (2017)

  43. Xie, S., Girshick, R., Dollr, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Computer vision and pattern recognition, CVPR 2017, pp. 5987–5995 (2017)

  44. Yang, Z., Li, Q., Li, Z., Ma, Y., Gong, Z., Liu, W.: Dual structure constrained multimodal feature coding for social event detection from flickr data. ACM transactions on internet technology (2017)

  45. Yang, Z., Li, Q., Lu, Z., Gong, Z., Liu, W.: Dual graph regularized NMF model for social event detection from flickr data. World Wide Web J 20(5), 995–1015 (2017)

    Article  Google Scholar 

  46. You, Q., Luo, J., Jin, H., Yang, J.: Cross-modality consistent regression for joint visual-textual sentiment analysis of social multimedia. In: Proceedings of the Ninth ACM international conference on Web search and data mining, pp. 13–22. ACM (2016)

  47. Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: A 10 million image database for scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61703109, No. 91748107), and the Guangdong Innovative Research Team Program (No. 2014ZT05G157).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China

    Zehang Lin, Feitao Huang, Yukun Li, Zhenguo Yang & Wenyin Liu

Authors
  1. Zehang Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Feitao Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yukun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhenguo Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wenyin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Zhenguo Yang or Wenyin Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, Z., Huang, F., Li, Y. et al. A layer-wise deep stacking model for social image popularity prediction. World Wide Web 22, 1639–1655 (2019). https://doi.org/10.1007/s11280-018-0590-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-018-0590-1

Keywords

Search

Navigation