Advertisement

From Web to Physical and Back: WP User Profiling with Deep Learning

  • Christian JoppiEmail author
  • Pietro Lovato
  • Marco Cristani
  • Gloria Menegaz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11153)

Abstract

This position paper discusses the definition and implementation of Web-Physical (WP) user profiles, which allow the creation of personalized recommendations and innovative behavioral predictions in particular scenarios, i.e., fairs. The nature of a WP profile builds upon two different worlds: the Web (social networks and web applications) and the Physical one, each one of them being explored through (big) data collection platforms. These two platforms collect radically different information: on the one hand, information of appreciation towards a particular product or service (web domain) together with other metadata; on the other, the leases (x, y) of users in the exhibition space (physical domain). In this scenario, our research idea consists in identifying how the information in the two domains can be merged in a whole entity under a theoretical point of view: this will unleash tangible repercussions in terms of personalized recommendations and effective behavioral predictions, where with personalized recommendation we mean a suggestion to a user in physical terms (eg a pavilion to visit) and / or in web terms (eg a site to visit) and with behavioral prediction a prediction of where a user can go in the future, even in a multimedia perspective (physical + web).

Keywords

Recommendation systems Multimedia Fairs 

References

  1. 1.
    Adeniyi, D., Wei, Z., Yongquan, Y.: Automated web usage data mining and recommendation system using k-nearest neighbor (KNN) classification method. Appl. Comput. Inf. 12(1), 90–108 (2016)Google Scholar
  2. 2.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005).  https://doi.org/10.1109/TKDE.2005.99CrossRefGoogle Scholar
  3. 3.
    Bello-Orgaz, G., Jung, J.J., Camacho, D.: Social big data: recent achievements and new challenges. Inf. Fusion 28, 45–59 (2016)CrossRefGoogle Scholar
  4. 4.
    Cao, X., Cong, G., Jensen, C.S.: Mining significant semantic locations from GPS data. Proc. VLDB Endow. 3(1–2), 1009–1020 (2010).  https://doi.org/10.14778/1920841.1920968CrossRefGoogle Scholar
  5. 5.
    Chen, L.H., Wu, E.H.K., Jin, M.H., Chen, G.H.: Intelligent fusion of wi-fi and inertial sensor-based positioning systems for indoor pedestrian navigation. IEEE Sens. J. 14(11), 4034–4042 (2014).  https://doi.org/10.1109/JSEN.2014.2330573CrossRefGoogle Scholar
  6. 6.
    Zhang, C., Li, H., Wang, X., Yang, X.: Cross-scene crowd counting via deep convolutional neural networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 2015 (2015)Google Scholar
  7. 7.
    Cristani, M., Burato, E., Santacá, K., Tomazzoli, C.: The spider-man behavior protocol: exploring both public and dark social networks for fake identity detection in terrorism informatics. In: CEUR Workshop Proceedings, vol. 1489, pp. 77–88 (2015)Google Scholar
  8. 8.
    Cristani, M., Fogoroasi, D., Tomazzoli, C.: Measuring homophily. In: CEUR Workshop Proceedings, vol. 1748 (2016)Google Scholar
  9. 9.
    Cristani, M., Olivieri, F., Tomazzoli, C.: Viral experiments. In: CEUR Workshop Proceedings, vol. 1959 (2017)Google Scholar
  10. 10.
    Ding, R., Chen, Z.: RecNet: a deep neural network for personalized poi recommendation in location-based social networks. Int. J. Geograph. Inf. Sci. 0(0), 1–18 (2018).  https://doi.org/10.1080/13658816.2018.1447671CrossRefGoogle Scholar
  11. 11.
    Dominguez, V., Messina, P., Parra, D., Mery, D., Trattner, C., Soto, A.: Comparing neural and attractiveness-based visual features for artwork recommendation. In: Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems, DLRS 2017, pp. 55–59. ACM, New York (2017).  https://doi.org/10.1145/3125486.3125495
  12. 12.
    Dumais, S., Jeffries, R., Russell, D.M., Tang, D., Teevan, J.: Understanding user behavior through log data and analysis. In: Olson, J.S., Kellogg, W.A. (eds.) Ways of Knowing in HCI, pp. 349–372. Springer, New York (2014).  https://doi.org/10.1007/978-1-4939-0378-8_14CrossRefGoogle Scholar
  13. 13.
    Elkahky, A.M., Song, Y., He, X.: A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: Proceedings of the 24th International Conference on World Wide Web, pp. 278–288. International World Wide Web Conferences Steering Committee (2015)Google Scholar
  14. 14.
    Farnadi, G., Tang, J., De Cock, M., Moens, M.F.: User profiling through deep multimodal fusion. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining. ACM (2018)Google Scholar
  15. 15.
    Herbig, P., O’Hara, B., Palumbo, F.: Differences between trade show exhibitors and non-exhibitors. J. Bus. Ind. Market. 12(6), 368–382 (1997)CrossRefGoogle Scholar
  16. 16.
    Ikeda, K., Hattori, G., Ono, C., Asoh, H., Higashino, T.: Twitter user profiling based on text and community mining for market analysis. Knowl.-Based Syst. 51, 35–47 (2013)CrossRefGoogle Scholar
  17. 17.
    Dhana Lakshmi, P., Ramani, K., Eswara Reddy, B.: Efficient techniques for clustering of users on web log data. In: Behera, H.S., Mohapatra, D.P. (eds.) Computational Intelligence in Data Mining. AISC, vol. 556, pp. 381–395. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-10-3874-7_35CrossRefGoogle Scholar
  18. 18.
    Li, R., Wang, S., Deng, H., Wang, R., Chang, K.C.C.: Towards social user profiling: unified and discriminative influence model for inferring home locations. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1023–1031. ACM (2012)Google Scholar
  19. 19.
    Maheswari, B.U., Sumathi, P.: A new clustering and preprocessing for web log mining. In: 2014 World Congress on Computing and Communication Technologies (WCCCT), pp. 25–29. IEEE (2014)Google Scholar
  20. 20.
    Munk, M., Kapusta, J., Švec, P.: Data preprocessing evaluation for web log mining: reconstruction of activities of a web visitor. Procedia Comput. Sci. 1(1), 2273–2280 (2010)CrossRefGoogle Scholar
  21. 21.
    Nasraoui, O., Soliman, M., Saka, E., Badia, A., Germain, R.: A web usage mining framework for mining evolving user profiles in dynamic web sites. IEEE Trans. Knowl. Data Eng. 20(2), 202–215 (2008)CrossRefGoogle Scholar
  22. 22.
    Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 689–696 (2011)Google Scholar
  23. 23.
    Williams, M.L., Burnap, P., Sloan, L.: Crime sensing with big data: the affordances and limitations of using open-source communications to estimate crime patterns. Br. J. Criminol. 57(2), 320–340 (2017)Google Scholar
  24. 24.
    Amatriain, X., Jaimes*, A., Oliver, N., Pujol, J.M.: Data mining methods for recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 39–71. Springer, Boston, MA (2011).  https://doi.org/10.1007/978-0-387-85820-3_2CrossRefGoogle Scholar
  25. 25.
    Yang, Y.C.: Web user behavioral profiling for user identification. Decis. Support Syst. 49(3), 261–271 (2010)CrossRefGoogle Scholar
  26. 26.
    Yin, H., Cui, B., Chen, L., Hu, Z., Zhang, C.: Modeling location-based user rating profiles for personalized recommendation. ACM Trans. Knowl. Discov. Data 9(3), 19:1–19:41 (2015).  https://doi.org/10.1145/2663356CrossRefGoogle Scholar
  27. 27.
    Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., Lipson, H.: Understanding neural networks through deep visualization. arXiv preprint arXiv:1506.06579 (2015)
  28. 28.
    Yuksel, U., Voola, R.: Travel trade shows: exploratory study of exhibitors’ perceptions. J. Bus. Ind. Market. 25(4), 293–300 (2010).  https://doi.org/10.1108/08858621011038252CrossRefGoogle Scholar
  29. 29.
    Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. 6(3), 29:1–29:41 (2015).  https://doi.org/10.1145/2743025CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Christian Joppi
    • 1
    Email author
  • Pietro Lovato
    • 1
  • Marco Cristani
    • 1
  • Gloria Menegaz
    • 1
  1. 1.Department of Computer ScienceUniversity of VeronaVeronaItaly

Personalised recommendations