Open Access
Issue
E3S Web of Conf.
Volume 388, 2023
The 4th International Conference of Biospheric Harmony Advanced Research (ICOBAR 2022)
Article Number 02002
Number of page(s) 4
Section Big Data, Green Computing, and Information System
DOI https://doi.org/10.1051/e3sconf/202338802002
Published online 17 May 2023
  1. Y. Yasuo, The evolution of the Japanese anime industry https://www.nippon.com/en/features/h00043/the-evolution-of-the-japanese-anime-industry. html (2022) [Google Scholar]
  2. K. Kirillova, C. Peng, H. Chen, Anime consumer motivation for anime tourism and how to harness it, J. Travel & Tourism Marketing 36, 2, pp. 268-281 (2019) [CrossRef] [Google Scholar]
  3. S. Napier, Anime from Akira to princess mononoke: experiencing contemporary Japanese animation (Springer, 2001) [CrossRef] [Google Scholar]
  4. J. Berndt, Anime in academia: representative object, media form, and Japanese studies, Arts 7, 4, pp. 56-69 (2018) [CrossRef] [Google Scholar]
  5. MyAnimeList.Co.Ltd, https://myanimelist.net/ (2022) [Google Scholar]
  6. L. Carter, Marketing anime to a global audience: a paratextual analysis of promotional materials from spirited away, East Asian J. Popular Culture 4, 1, pp. 47-59 (2018) [CrossRef] [Google Scholar]
  7. R. Mihara, A coming of age in the anthropological study of anime? J. Business Anthropology 9, 1, pp. 88-110 (2020) [CrossRef] [Google Scholar]
  8. S. M. AlSulaim, A. M. Qamar, Prediction of anime series’ success using sentiment analysis and deep learning, 2021 International Conference of Women in Data Science at Taif University (WiDSTaif), pp. 1–6 (2021) [Google Scholar]
  9. A. S. Girsang, B. Al Faruq, H. R. Herlianto, S. Simbolon, Collaborative recommendation system in users of anime films, J. Physics: Conference Series 1566, 1, pp. 012057 (2020) [CrossRef] [Google Scholar]
  10. V. M. Mutteppagol, A deep learning recommender system for anime, Ph.D. dissertation (National College of Ireland, Dublin, 2021) [Google Scholar]
  11. A. T. Wibowo, Leveraging side information to anime recommender system using deep learning, 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pp. 62-67 (2020) [Google Scholar]
  12. J. Taylor, B. Letham, Forecasting at scale (PeerJPreprints, 2017) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.