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Content-based artwork recommendation: integrating painting metadata with neural and manually-engineered visual features

  • Pablo Messina
  • Vicente Dominguez
  • Denis Parra
  • Christoph Trattner
  • Alvaro Soto
Article

Abstract

Recommender Systems help us deal with information overload by suggesting relevant items based on our personal preferences. Although there is a large body of research in areas such as movies or music, artwork recommendation has received comparatively little attention, despite the continuous growth of the artwork market. Most previous research has relied on ratings and metadata, and a few recent works have exploited visual features extracted with deep neural networks (DNN) to recommend digital art. In this work, we contribute to the area of content-based artwork recommendation of physical paintings by studying the impact of the aforementioned features (artwork metadata, neural visual features), as well as manually-engineered visual features, such as naturalness, brightness and contrast. We implement and evaluate our method using transactional data from UGallery.com, an online artwork store. Our results show that artwork recommendations based on a hybrid combination of artist preference, curated attributes, deep neural visual features and manually-engineered visual features produce the best performance. Moreover, we discuss the trade-off between automatically obtained DNN features and manually-engineered visual features for the purpose of explainability, as well as the impact of user profile size on predictions. Our research informs the development of next-generation content-based artwork recommenders which rely on different types of data, from text to multimedia.

Keywords

Artwork Recommender systems Content-based recommender Hybrid recommendations Metadata Visual features Deep neural networks 

Notes

Acknowledgements

This research has been supported by the Chilean research agency Conicyt, under Fondecyt Grant 11150783, and partially funded by the Millennium Institute for Foundational Research on Data (IMFD). We also acknowledge the help from Felipe del Río and Domingo Mery, who helped us frame some evaluations and provided us with some interesting ideas for future work.

References

  1. 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)CrossRefGoogle Scholar
  2. Aggarwal, C.C.: Content-based recommender systems. In: Recommender Systems, pp. 139–166. Springer, Berlin (2016).  https://doi.org/10.1007/978-3-319-29659-3_4
  3. Akay, S., Kundegorski, M.E., Devereux, M., Breckon, T.P.: Transfer learning using convolutional neural networks for object classification within X-ray baggage security imagery. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 1057–1061 (2016)Google Scholar
  4. Albanese, M., d’Acierno, A., Moscato, V., Persia, F., Picariello, A.: A multimedia semantic recommender system for cultural heritage applications. In: Proceedings of the Fifth IEEE International Conference on Semantic Computing (ICSC), pp. 403–410 (2011)Google Scholar
  5. Amatriain, X.: Mining large streams of user data for personalized recommendations. ACM SIGKDD Explor. Newsl. 14(2), 37–48 (2013)CrossRefGoogle Scholar
  6. Aroyo, L., Wang, Y., Brussee, R., Gorgels, P., Rutledge, L., Stash, N.: Personalized museum experience: the rijksmuseum use case. In: Proceedings of Museums and the Web (2007)Google Scholar
  7. Bennett, J., Lanning, S., et al.: The netflix prize. In: Proceedings of KDD Cup and Workshop, vol. 2007, p. 35 (2007)Google Scholar
  8. Benouaret, I., Lenne, D.: Personalizing the museum experience through context-aware recommendations. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 743–748 (2015)Google Scholar
  9. Celma, O.: Music recommendation. In: Music Recommendation and Discovery, pp. 43–85. Springer, Berlin (2010).  https://doi.org/10.1007/978-3-642-13287-2_3
  10. 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, RecSys ’10, pp. 39–46 (2010)Google Scholar
  11. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR) 1, 886–893 (2005)Google Scholar
  12. David, O.E., Netanyahu, N.S.: DeepPainter: Painter Classification Using Deep Convolutional Autoencoders, pp. 20–28. Springer, Berlin (2016)Google Scholar
  13. de Gemmis, M., Lops, P., Musto, C., Narducci, F., Semeraro, G.: Semantics-aware content-based recommender systems. In: Recommender Systems Handbook, pp. 119–159. Springer, Berlin (2015)Google Scholar
  14. Deldjoo, Y., Elahi, M., Cremonesi, P., Garzotto, F., Piazzolla, P., Quadrana, M.: Content-based video recommendation system based on stylistic visual features. J. Data Semant. 5(2), 99–113 (2016)CrossRefGoogle Scholar
  15. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248–255 (2009)Google Scholar
  16. Ekstrand, M.D., Kluver, D., Harper, F.M., Konstan, J.A.: Letting users choose recommender algorithms: an experimental study. In: Proceedings of the 9th ACM Conference on Recommender Systems, RecSys ’15, pp. 11–18 (2015).  https://doi.org/10.1145/2792838.2800195
  17. Elahi, M., Deldjoo, Y., Bakhshandegan Moghaddam, F., Cella, L., Cereda, S., Cremonesi, P.: Exploring the semantic gap for movie recommendations. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys’17, pp. 326–330 (2017)Google Scholar
  18. Esman, A.R.: The World’s Strongest Economy? The Global Art Market. https://www.forbes.com/sites/abigailesman/2012/02/29/the-worlds-strongest-economy-the-global-art-market/ (2012). Accessed 21 March 2017
  19. Gatys, L.A., Ecker, A.S., Bethge, M.: A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576 (2015)
  20. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)Google Scholar
  21. Gomez-Uribe, C.A., Hunt, N.: The netflix recommender system: algorithms, business value, and innovation. ACM Trans. Manag. Inf. Syst. (TMIS) 6(4), 13 (2016)Google Scholar
  22. Gonzalez, R.C., Eddins, S.L., Woods, R.E.: Digital Image Publishing Using MATLAB. Prentice Hall, Upper Saddle River (2004)Google Scholar
  23. Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 19:1–19:19 (2015)CrossRefGoogle Scholar
  24. He, R., Fang, C., Wang, Z., McAuley, J.: Vista: A visually, socially, and temporally-aware model for artistic recommendation. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys ’16, pp. 309–316 (2016)Google Scholar
  25. He, R., McAuley, J.: VBPR: Visual Bayesian personalized ranking from implicit feedback. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp. 144–150 (2016)Google Scholar
  26. Hidasi, B., Quadrana, M., Karatzoglou, A., Tikk, D.: Parallel recurrent neural network architectures for feature-rich session-based recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys’16, pp. 241–248 (2016)Google Scholar
  27. Kannala, J., Rahtu, E.: Bsif: Binarized statistical image features. In: Proceedings of 21st International Conference on Pattern Recognition (ICPR), pp. 1363–1366 (2012)Google Scholar
  28. Karnowski, J.: AlexNet + SVM. https://jeremykarnowski.files.wordpress.com/2015/07/alexnet2.png (2015). Accessed 1 Dec 2017
  29. Knijnenburg, B.P., Bostandjiev, S., O’Donovan, J., Kobsa, A.: Inspectability and control in social recommenders. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 43–50 (2012)Google Scholar
  30. Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: Proceedings of ICML Deep Learning Workshop, vol. 2 (2015)Google Scholar
  31. Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User Adapt. Interact. 22(1–2), 101–123 (2012)CrossRefGoogle Scholar
  32. Koren, Y.: Collaborative filtering with temporal dynamics. Commun. ACM 53(4), 89–97 (2010)CrossRefGoogle Scholar
  33. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems 25(NIPS), pp. 1097–1105 (2012)Google Scholar
  34. La Cascia, M., Sethi, S., Sclaroff, S.: Combining textual and visual cues for content-based image retrieval on the world wide web. In: Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries, pp. 24–28 (1998)Google Scholar
  35. Lacic, E., Kowald, D., Eberhard, L., Trattner, C., Parra, D., Marinho, L.B.: Utilizing online social network and location-based data to recommend products and categories in online marketplaces. In: Atzmueller M., Chin A., Scholz C., Trattner C. (eds) Mining, Modeling, and Recommending ‘Things’ in Social Media. MUSE 2013, MSM 2013. Lecture Notes in Computer Science, vol 8940. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-14723-9_6
  36. Larrain, S., Trattner, C., Parra, D., Graells-Garrido, E., Nørvåg, K.: Good times bad times: a study on recency effects in collaborative filtering for social tagging. In: Proceedings of the 9th ACM Conference on Recommender Systems, RecSys’15, pp. 269–272 (2015)Google Scholar
  37. Lei, C., Liu, D., Li, W., Zha, Z.J., Li, H.: Comparative deep learning of hybrid representations for image recommendations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2545–2553 (2016)Google Scholar
  38. Liao, W.H., Young, T.J.: Texture classification using uniform extended local ternary patterns. In: Proceedings of IEEE International Symposium on Multimedia (ISM), pp. 191–195 (2010)Google Scholar
  39. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  40. Maaten, L.V.D., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008)zbMATHGoogle Scholar
  41. Macedo, A.Q., Marinho, L.B., Santos, R.L.: Context-aware event recommendation in event-based social networks. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 123–130 (2015)Google Scholar
  42. Maes, P., et al.: Agents that reduce work and information overload. Commun. ACM 37(7), 30–40 (1994)CrossRefGoogle Scholar
  43. Manning, C.D., Raghavan, P., Schütze, H., et al.: Introduction to Information Retrieval, vol. 1. Cambridge University Press Cambridge, Cambridge (2008)CrossRefzbMATHGoogle Scholar
  44. Mathieu, M.F., Zhao, J.J., Zhao, J., Ramesh, A., Sprechmann, P., LeCun, Y.: Disentangling factors of variation in deep representation using adversarial training. In: Proceedings of Advances in Neural Information Processing Systems, pp. 5040–5048 (2016)Google Scholar
  45. McAuley, J., Targett, C., Shi, Q., Van Den Hengel, A.: Image-based recommendations on styles and substitutes. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 43–52 (2015)Google Scholar
  46. Mensink, T., Van Gemert, J.: The rijksmuseum challenge: Museum-centered visual recognition. In: Proceedings of International Conference on Multimedia Retrieval, p. 451 (2014)Google Scholar
  47. Nguyen, A., Yosinski, J., Clune, J.: Multifaceted feature visualization: uncovering the different types of features learned by each neuron in deep neural networks. arXiv preprint arXiv:1602.03616 (2016)
  48. Nunes, I., Jannach, D.: A systematic review and taxonomy of explanations in decision support and recommender systems. User Model. User Adapt. Interact. 27(3), 393–444 (2017)CrossRefGoogle Scholar
  49. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  50. Olah, C., Mordvintsev, A., Schubert, L.: Feature visualization. Distill (2017) .  https://doi.org/10.23915/distill.00007
  51. Parra, D., Brusilovsky, P.: User-controllable personalization: a case study with setfusion. Int. J. Hum. Comput. Stud. 78, 43–67 (2015)CrossRefGoogle Scholar
  52. Parra, D., Sahebi, S.: Recommender systems: sources of knowledge and evaluation metrics. In: Advanced Techniques in Web Intelligence-2, pp. 149–175. Springer, Berlin (2013).  https://doi.org/10.1007/978-3-642-33326-2_7
  53. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
  54. Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461 (2009)Google Scholar
  55. Rui, Y., Huang, T.S., Ortega, M., Mehrotra, S.: Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans. Circuits Syst. Video Technol. 8(5), 644–655 (1998)CrossRefGoogle Scholar
  56. Rush, J.C.: Acquiring a concept of painting style. Stud. Art Educ. 20(3), 43–51 (1979)CrossRefGoogle Scholar
  57. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  58. San Pedro, J., Siersdorfer, S.: Ranking and classifying attractiveness of photos in folksonomies. In: Proceedings of the 18th International Conference on World Wide Web, WWW’09, pp. 771–780 (2009)Google Scholar
  59. Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)Google Scholar
  60. Semeraro, G., Lops, P., De Gemmis, M., Musto, C., Narducci, F.: A folksonomy-based recommender system for personalized access to digital artworks. J. Comput. Cult. Herit. (JOCCH) 5(3), 11 (2012)Google Scholar
  61. Shankar, D., Narumanchi, S., Ananya, H., Kompalli, P., Chaudhury, K.: Deep learning based large scale visual recommendation and search for e-commerce. arXiv preprint arXiv:1703.02344 (2017)
  62. Sharif Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 806–813 (2014)Google Scholar
  63. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  64. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of AAAI, vol. 4, p. 12 (2017)Google Scholar
  65. Tintarev, N., Masthoff, J.: Explaining recommendations: design and evaluation. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook. Springer, Boston (2015).  https://doi.org/10.1007/978-1-4899-7637-6_10 Google Scholar
  66. Trattner, C., Elsweiler, D.: Investigating the healthiness of internet-sourced recipes: implications for meal planning and recommender systems. In: Proceedings of the 26th International Conference on World Wide Web, pp. 489–498 (2017)Google Scholar
  67. Verbert, K., Parra, D., Brusilovsky, P., Duval, E.: Visualizing recommendations to support exploration, transparency and controllability. In: Proceedings of the 2013 International Conference on Intelligent User Interfaces, pp. 351–362 (2013)Google Scholar
  68. Wang, J., Song, Y., Leung, T., Rosenberg, C., Wang, J., Philbin, J., Chen, B., Wu, Y.: Learning fine-grained image similarity with deep ranking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1386–1393 (2014)Google Scholar
  69. Weinswig, D.: Art Market Cooling, But Online Sales Booming. https://www.forbes.com/sites/deborahweinswig/2016/05/13/art-market-cooling-but-online-sales-booming/ (2016). Accessed 21 March 2017
  70. Yang, L., Cui, Y., Zhang, F., Pollak, J.P., Belongie, S., Estrin, D.: PlateClick: bootstrapping food preferences through an adaptive visual interface. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM’15, pp. 183–192 (2015)Google Scholar

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© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.IMFDSantiagoChile
  2. 2.Pontificia Universidad Católica (PUC)SantiagoChile
  3. 3.University of BergenBergenNorway

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