Abstract
Visual information is one of the essential information for human to understand the real world. The abundant information of visual features absolutely give people many useful inferences. In movie recommendation, additional information extracted is undoubtedly beneficial for alleviating the drawback that sparseness of rating data leads to. Obviously, the visual information is such supplementary information in movie recommendation in particular. Existing context-aware movie recommendation methods normally focused on the complementary information to address the problem caused by sparseness, such as social relationship between users, reviews, attributes of movie itself. Nevertheless, there are merely a small part of researches concentrating on visual features compared to the information above. The reasons may come from two aspects: (i) the difficulties of getting useful information from movies; (ii) the difficulties of finding a proper dataset. Nowadays, the outstanding development of deep learning in computer vision fortunately help us out in the first problem. As for the second difficulty, based on the mature dataset MovieLens, we rebuild the dataset by adding movie trailers crawling from YouTube. In this paper, we propose a novel Probabilistic Matrix Factorization (PMF) model incorporating the visual information of trailers, Visual information based PMF (VPMF). Based on classic recommendation model PMF, the VPMF extracts visual features from trailers to enrich the core information furthermore ensure the accuracy. At last, a VPMF recommender system architecture is given to show how the system works.
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References
Abu-El-Haija, S., Kothari, N., Lee, J., Natsev, P., Toderici, G., Varadarajan, B., Vijayanarasimhan, S.: Youtube-8M: a large-scale video classification benchmark. arXiv preprint arXiv:1609.08675 (2016)
Cheng, R., Tang, B.: A music recommendation system based on acoustic features and user personalities. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 203–213. Springer International Publishing (2016)
Deng, S., Wang, D., Li, X., Xu, G.: Exploring user emotion in microblogs for music recommendation. Expert Syst. Appl. 42(23), 9284–9293 (2015)
Hariri, N., Mobasher, B., Burke, R.: Context-aware music recommendation based on latent topic sequential patterns. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 131–138. ACM (2012)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3128–3137 (2015)
Kim, D., Park, C., Oh, J., Lee, S., Yu, H.: Convolutional matrix factorization for document context-aware recommendation. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 233–240. ACM (2016)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Pan, P., Xu, Z., Yang, Y., Wu, F., Zhuang, Y.: Hierarchical recurrent neural encoder for video representation with application to captioning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1029–1038 (2016)
Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Advances in neural information processing systems, pp. 1257–1264 (2008)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: a neural image caption generator. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3156–3164 (2015)
Yu, H., Wang, J., Huang, Z., Yang, Y., Xu, W.: Video paragraph captioning using hierarchical recurrent neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4584–4593 (2016)
Zhao, L., Lu, Z., Pan, S.J., Yang, Q.: Matrix factorization+ for movie recommendation. In: IJCAI 2016, New York, USA (2016)
Zhao, W.X., Li, S., He, Y., Chang, E.Y., Wen, J.R., Li, X.: Connecting social media to e-commerce: cold-start product recommendation using microblogging information. IEEE Trans. Knowl. Data Eng. 28(5), 1147–1159 (2016)
Zhao, W.X., Wang, J., He, Y., Wen, J.R., Chang, E.Y., Li, X.: Mining product adopter information from online reviews for improving product recommendation. ACM Trans. Knowl. Discov. Data (TKDD) 10(3), 29 (2016)
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This paper is supported by National Natural Science Foundation of China (Project 61372113).
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Fan, Y., Wang, Y., Yu, H., Liu, B. (2018). Movie Recommendation Based on Visual Features of Trailers. In: Barolli, L., Enokido, T. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing . IMIS 2017. Advances in Intelligent Systems and Computing, vol 612. Springer, Cham. https://doi.org/10.1007/978-3-319-61542-4_23
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DOI: https://doi.org/10.1007/978-3-319-61542-4_23
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