Ukiyo-e Recommendation based on Deep Learning For Learning Japanese Art and Culture

This paper describes an Ukiyo-e recommendation based on deep learning to obtain images for Ukiyo-e novices. Although some Ukiyo-e web search services are available, owing to the recent increase in Ukiyo-e's world popularity, finding appropriate Ukiyo-e prints that are interesting to a novice is difficult. One reason for this is that recommendation support for Ukiyo-e prints is not currently available. Therefore, this paper describes a deep-learning-based Ukiyo-e recommendation that can present preferential prints to novices.

[1]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[2]  Jürgen Schmidhuber,et al.  Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[4]  Gerhard Friedrich,et al.  Recommender Systems - An Introduction , 2010 .

[5]  Jeff Donahue,et al.  Visual Search at Pinterest , 2015, KDD.

[6]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[7]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Alexandros Karatzoglou,et al.  Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations , 2016, RecSys.

[9]  Paul Covington,et al.  Deep Neural Networks for YouTube Recommendations , 2016, RecSys.

[10]  Yi Yang,et al.  A Kind of Precision Recommendation Method for Massive Public Digital Cultural Resources: A Preliminary Report , 2016, 2016 IEEE Second International Conference on Multimedia Big Data (BigMM).

[11]  Yoshua Bengio,et al.  Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.

[12]  Taketoshi Ushiama,et al.  An exhibit recommendation system based on semantic networks for museum , 2012 .

[13]  Donghyun Kim,et al.  Convolutional Matrix Factorization for Document Context-Aware Recommendation , 2016, RecSys.