Advertisement

Visual Perception Similarities to Improve the Quality of User Cold Start Recommendations

  • Crícia Z. Felício
  • Claudianne M. M. de Almeida
  • Guilherme Alves
  • Fabíola S. F. Pereira
  • Klérisson V. R. Paixão
  • Sandra de Amo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9673)

Abstract

Recommender systems are well-know for taking advantage of available personal data to provide us information that best fit our interests. However, even after the explosion of social media on the web, hence personal information, we are still facing new users without any information. This problem is known as user cold start and is one of the most challenging problems in this field. We propose a novel approach, VP-Similarity, based on human visual attention for addressing this problem. Our algorithm computes visual perception’s similarities among users to build a visual perception network. Then, this networked information is provided to recommender system to generate recommendations. Experimental results validated that VP-Similarity achieves high-quality ranking results for user cold start recommendation.

Keywords

Visual perception Cold start Recommender system 

References

  1. 1.
    Sahebi, S., Cohen, W.: Community-based recommendations: a solution to the cold start problem. In: Workshop on Recommender Systems and the Social Web (RSWEB), held in conjunction with ACM RecSys 2011 (2011)Google Scholar
  2. 2.
    Felício, C.Z., Paixão, K.V.R., Alves, G., de Amo, S.: Social prefrec framework: leveraging recommender systems based on social information. In: Proceedings of the 3rd Symposium on Knowledge Discovery, Mining and Learning, pp. 66–73 (2015)Google Scholar
  3. 3.
    Ma, H., Yang, H., Lyu, M.R., King, I.: Sorec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM 2008, pp. 931–940. ACM (2008)Google Scholar
  4. 4.
    Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys 2010, pp. 135–142. ACM (2010)Google Scholar
  5. 5.
    Yang, B., Lei, Y., Liu, D., Liu, J.: Social collaborative filtering by trust. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence, IJCAI, pp. 2747–2753 (2013)Google Scholar
  6. 6.
    Guo, G., Zhang, J., Sun, Z., Yorke-Smith, N.: Librec: a java library for recommender systems. In: 23rd User Modeling, Adaptation, and Personalization (2015)Google Scholar
  7. 7.
    Victor, P., Cornelis, C., Teredesai, A.M., De Cock, M.: Whom should i trust?: the impact of key figures on cold start recommendations. In: Proceedings of the 2008 ACM Symposium on Applied Computing, SAC 2008, pp. 2014–2018. ACM (2008)Google Scholar
  8. 8.
    de Amo, S., Oliveira, C.G.: Towards a tunable framework for recommendation systems based on pairwise preference mining algorithms. In: Sokolova, M., van Beek, P. (eds.) Canadian AI. LNCS, vol. 8436, pp. 282–288. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  9. 9.
    Sugano, Y., Ozaki, Y., Kasai, H., Ogaki, K., Sato, Y.: Image preference estimation with a data-driven approach: a comparative study between gaze and image features. J. Eye Mov. Res. 7(3), 1–9 (2014)Google Scholar
  10. 10.
    de Melo, V.E., Nogueira, E.A., Guliato, D.: Content-based filtering enhanced by human visual attention applied to clothing recommendation. In: IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 644–651 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Crícia Z. Felício
    • 1
    • 3
  • Claudianne M. M. de Almeida
    • 2
    • 3
  • Guilherme Alves
    • 3
  • Fabíola S. F. Pereira
    • 3
  • Klérisson V. R. Paixão
    • 3
  • Sandra de Amo
    • 3
  1. 1.Federal Institute of Triângulo MineiroUberlândiaBrazil
  2. 2.Federal Institute of Norte de Minas ArinosBrazil
  3. 3.Federal University of UberlândiaUberlândiaBrazil

Personalised recommendations