Abstract
In the past decades, many digital archives are developed for storing cultural materials such as arts and books. Art Research Center (ARC) at Ritsumeikan University has developed digital archives for various Japanese ancient cultural materials. The ARC digital archive preserves a large amount of Ukiyo-e artworks. In this paper, we propose a recommender system that is suitable for the ARC Ukiyo-e digital archive, to help the users obtaining the interested Ukiyo-e artworks conveniently. The recommender algorithm is based on the user log data, which is easy to obtain and represents the user behaviors. The proposed method is named CARC, which uses restricted Boltzmann machine (RBM) for collaborative filtering to initialize the recommendation list, and then uses content-based filtering (CBF) for generating more explicit recommendation list. The proposed recommender system is effective to extract the pattern of users’ behaviors and construct the recommendation list that fits the taste of users.
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Wang, J., Batjargal, B., Maeda, A., Kawagoe, K. (2018). A Recommender System in Ukiyo-e Digital Archives for Japanese Art Novices. In: Dobreva, M., Hinze, A., Žumer, M. (eds) Maturity and Innovation in Digital Libraries. ICADL 2018. Lecture Notes in Computer Science(), vol 11279. Springer, Cham. https://doi.org/10.1007/978-3-030-04257-8_22
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DOI: https://doi.org/10.1007/978-3-030-04257-8_22
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