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.
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Ji, Z., Tang, J., Wu, G. (2019). Personalized Recommendation of Photography Based on Deep Learning. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11295. Springer, Cham. https://doi.org/10.1007/978-3-030-05710-7_18
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DOI: https://doi.org/10.1007/978-3-030-05710-7_18
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