Personalised Dynamic Viewer Profiling for Streamed Data

  • Bruno Veloso
  • Benedita Malheiro
  • Juan Carlos Burguillo
  • Jeremy Foss
  • João Gama
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)

Abstract

Nowadays, not only the number of multimedia resources available is increasing exponentially, but also the crowd-sourced feedback volunteered by viewers generates huge volumes of ratings, likes, shares and posts/reviews. Since the data size involved surpasses human filtering and searching capabilities, there is the need to create and maintain the profiles of viewers and resources to develop recommendation systems to match viewers with resources. In this paper, we propose a personalised viewer profiling technique which creates individual viewer models dynamically. This technique is based on a novel incremental learning algorithm designed for stream data. The results show that our approach outperforms previous approaches, reducing substantially the prediction errors and, thus, increasing the accuracy of the recommendations.

Keywords

On-line viewer profiling Data stream mining Personalisation 

Notes

Acknowledgements

This research was carried out in the framework of the project TEC4Growth – RL SMILES –Smart, mobile, Intelligent and Large Scale Sensing and analytics NORTE-01-0145-FEDER-000020 which is financed by the north Portugal regional operational program (NORTE 2020), under the Portugal 2020 partnership agreement, and through the European regional development fund.

References

  1. 1.
    Barragáns-Martínez, A.B., Costa-Montenegro, E., Burguillo, J.C., Rey-López, M., Mikic-Fonte, F.A., Peleteiro, A.: A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition. Inf. Sci. 180(22), 4290–4311 (2010)CrossRefGoogle Scholar
  2. 2.
    Billsus, D., Pazzani, M.J.: Learning collaborative information filters. In: Proceedings of the 15th International Conference on Machine Learning, ICML 1998, vol. 98, pp. 46–54. Morgan Kaufmann Publishers Inc. (1998)Google Scholar
  3. 3.
    Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 39–46. ACM (2010)Google Scholar
  4. 4.
    Gama, J.: Knowledge Discovery from Data Streams. Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. CRC Press, Florida (2010)CrossRefGoogle Scholar
  5. 5.
    Gama, J., Sebastião, R., Rodrigues, P.P.: Issues in evaluation of stream learning algorithms. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 329–338. ACM (2009)Google Scholar
  6. 6.
    Gower, S.: Netflix prize and SVD (2014)Google Scholar
  7. 7.
    Matuszyk, P., Vinagre, J., Spiliopoulou, M., Jorge, A.M., Gama, J.: Forgetting methods for incremental matrix factorisation in recommender systems. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing, SAC 2015, pp. 947–953. ACM, New York (2015)Google Scholar
  8. 8.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Application of dimensionality reduction in recommender system-a case study. Technical report, DTIC Document (2000)Google Scholar
  9. 9.
    Takács, G., Pilászy, I., Németh, B., Tikk, D.: Scalable collaborative filtering approaches for large recommender systems. J. Mach. Learn. Res. 10, 623–656 (2009)Google Scholar
  10. 10.
    Veloso, B., Malheiro, B., Burguillo, J.C., Foss, J.: Personalised fading for stream data. In: Proceedings of the Symposium on Applied Computing, SAC 2017, pp. 870–872. ACM, New York (2017)Google Scholar
  11. 11.
    Vinagre, J., Jorge, A.M., Gama, J.: Fast incremental matrix factorisation for recommendation with positive-only feedback. In: Dimitrova, V., Kuik, T., Chin, D., Ricci, F., Dolog, p., Houben, G.-J. (eds.) Proceedings of the 22nd International Conference User Modeling, Adaptation, and Personalization, UMAP 2014, pp. 459–470. Springer, Cham (2014)Google Scholar
  12. 12.
    Vinagre, J., Jorge, A.M., Gama, J.: Collaborative filtering with recency-based negative feedback. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing, SAC 2015, pp. 963–965. ACM, New York (2015)Google Scholar
  13. 13.
    Xiang, L., Yang, Q.: Time-dependent models in collaborative filtering based recommender system. In: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01, WI-IAT 2009, pp. 450–457, Washington, DC, USA (2009)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Bruno Veloso
    • 1
  • Benedita Malheiro
    • 2
    • 3
  • Juan Carlos Burguillo
    • 4
  • Jeremy Foss
    • 5
  • João Gama
    • 1
    • 6
  1. 1.LIAAD - INESC TECPortoPortugal
  2. 2.ISEP - Polytechnic Institute of PortoPortoPortugal
  3. 3.CRAS - INESC TECPortoPortugal
  4. 4.EET - University of VigoVigoSpain
  5. 5.CEBE - Birmingham City UniversityBirminghamUK
  6. 6.FEP - Universidade do PortoPortoPortugal

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