Advertisement

Personalized Recommendation of Photography Based on Deep Learning

  • Zhixiang Ji
  • Jie TangEmail author
  • Gangshan Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11295)

Abstract

The key to the picture recommendation problem lies in the representation of image features. There are many methods for image feature description, and some are mature. However, due to the particularity of the photographic works we are concerned with, the traditional recommendation based on original features or labels cannot get better results. In our topic problem, the discovery of image style features is very important. Our main job is to propose an optimized feature representation method in the unlabeled data set, and to train by the deep learning convolutional neural network (CNN), and finally achieve the recommended purpose. Combined with the latent factor model, the user features and image style features are deeply characterized. After a lot of experiments, we show that our method is better than other mainstream recommendation algorithms based on unlabeled data sets, and achieved better recommendation results.

Keywords

Photography Recommendation Image style 

References

  1. 1.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 88–893 (2005)Google Scholar
  2. 2.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)CrossRefGoogle Scholar
  3. 3.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, pp. 1097-1105 (2012)Google Scholar
  4. 4.
    Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Computer Vision and Pattern Recognition, pp. 2414–2423 (2016)Google Scholar
  5. 5.
    Lu, X., Lin, Z., Shen, X., Mech, R., Wang, J.Z.: Deep multi-patch aggregation network for image style, aesthetics, and quality estimation. In: IEEE International Conference on Computer Vision, pp. 990-998 (2015)Google Scholar
  6. 6.
    Tang, L., Chang, J.Y., Li, J., Yu, R.W.: A new accelerated algorithm of image style study. In: International Conference on Multimedia Information Networking and Security, pp. 244–248 (2009)Google Scholar
  7. 7.
    Sun, T., Wang, Y., Yang, J., Hu, X.: Convolution neural networks with two pathways for image style recognition. In: IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society, pp. 4102–4113 (2017)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40, 66–72 (1997)CrossRefGoogle Scholar
  9. 9.
    Saveski, M., Mantrach, A.: Item cold-start recommendations: learning local collective embeddings. In: ACM Conference on Recommender Systems, pp. 89-96 (2014)Google Scholar
  10. 10.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell., 2 (2009)Google Scholar
  11. 11.
    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
  12. 12.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Eighth IEEE International Conference on Data Mining, pp. 263–272 (2009)Google Scholar
  13. 13.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: International Conference on Neural Information Processing Systems, pp. 1257–1264 (2007)Google Scholar
  14. 14.
    Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 448–456 (2011)Google Scholar
  15. 15.
    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, 4290–4311 (2010)CrossRefGoogle Scholar
  16. 16.
    Geng, X., Zhang, H., Bian, J., Chua, T.S.: Learning image and user features for recommendation in social networks. In: IEEE International Conference on Computer Vision, pp. 4274-4282 (2015)Google Scholar
  17. 17.
    Dieleman, S., Schrauwen, B.: Deep content-based music recommendation. In: Advances in Neural Information Processing Systems, pp. 2643–2651 (2013)Google Scholar
  18. 18.
    Jenatton, R., Roux, N.L., Bordes, A., Obozinski, G.: A latent factor model for highly multi-relational data. In: International Conference on Neural Information Processing Systems, pp. 3167-3175 (2012)Google Scholar
  19. 19.
    Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. ACM Trans. Inf. Syst., 5–53 (2004)Google Scholar
  20. 20.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097-1105 (2012)Google Scholar
  21. 21.
    Liu, L., Özsu, M.T.: Mean average precision. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems. Springer, Boston (2009).  https://doi.org/10.1007/978-0-387-39940-9_3032CrossRefzbMATHGoogle Scholar
  22. 22.
    Baddeley, A.: Area under ROC Curve. http://www.packages.ianhowson.com

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyNanjing UniversityNanjingChina
  2. 2.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

Personalised recommendations