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Analyzing User Requests for Anime Recommendations

Authors:

Jin Ha Lee,

Yuna Shim,

Jacob JettAuthors Info & Claims

JCDL '15: Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries

Pages 269 - 270

https://doi.org/10.1145/2756406.2756969

Published: 21 June 2015 Publication History

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Abstract

Anime is increasingly becoming recognized as an important commercial product and cultural artifact. However, little is known regarding users' information needs and behavior related to anime. This study specifically attempts to improve our understanding of how people seek anime recommendations. We analyzed 546 user questions in natural language, collected from a Korean Q&A website Naver Knowledge-iN, where users are asking for anime recommendations. The findings suggest the importance of establishing robust metadata for the seven commonly used features for anime recommenders (i.e., title, genre, artistic style, story, character description, series title, and mood) in digital libraries, as well as allowing users to specify known anime and series titles as examples for seeking similar items, or examples of the kinds of items to be excluded.

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Cited By

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Lee WHirt JCho H(2024)From Fandom to Fadeaway: Unpacking Factors Contributing to Anime DropoutProceedings of the Association for Information Science and Technology10.1002/pra2.116361:1(989-991)Online publication date: 15-Oct-2024
https://doi.org/10.1002/pra2.1163
Ashwin Pillai Advin Manhar (2023)Unveiling Anime Preferences: A Data-driven Analysis using MyAnimeList APIInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT23903115(488-495)Online publication date: 1-Jun-2023
https://doi.org/10.32628/CSEIT23903115
Homma NUemura AKitahara T(2021)Are Theme Songs Usable for Anime Retrieval?2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR51284.2021.00042(227-230)Online publication date: Sep-2021
https://doi.org/10.1109/MIPR51284.2021.00042
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Analyzing User Requests for Anime Recommendations

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Published In

JCDL '15: Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries

June 2015

324 pages

ISBN:9781450335942

DOI:10.1145/2756406

General Chairs:
Paul Logasa Bogen
Google, USA
,
Suzie Allard
School of Information Sciences, University of Tennessee, USA
,
Holly Mercer
University Libraries, University of Tennessee, USA
,
Micah Beck
University of Tennessee, USA
,
Program Chairs:
Sally Jo Cunningham
Waikato University, New Zealand
,
Dion Goh
Wee Kim Wee School of Communication and Information, Nanyang Technical University, Singapore
,
Geneva Henry
University Libraries, The George Washington University, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 21 June 2015

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JCDL '15: 15th ACM/IEEE-CS Joint Conference on Digital Libraries

June 21 - 25, 2015

Tennessee, Knoxville, USA

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JCDL '15 Paper Acceptance Rate 18 of 60 submissions, 30%;

Overall Acceptance Rate 415 of 1,482 submissions, 28%

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Lee WHirt JCho H(2024)From Fandom to Fadeaway: Unpacking Factors Contributing to Anime DropoutProceedings of the Association for Information Science and Technology10.1002/pra2.116361:1(989-991)Online publication date: 15-Oct-2024
https://doi.org/10.1002/pra2.1163
Ashwin Pillai Advin Manhar (2023)Unveiling Anime Preferences: A Data-driven Analysis using MyAnimeList APIInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT23903115(488-495)Online publication date: 1-Jun-2023
https://doi.org/10.32628/CSEIT23903115
Homma NUemura AKitahara T(2021)Are Theme Songs Usable for Anime Retrieval?2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR51284.2021.00042(227-230)Online publication date: Sep-2021
https://doi.org/10.1109/MIPR51284.2021.00042
Kiryakos SPfeffer M(2021)Exploring the Research Utility of Fan-Created Data in the Japanese Visual Media DomainTowards Open and Trustworthy Digital Societies10.1007/978-3-030-91669-5_17(210-218)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1007/978-3-030-91669-5_17
Cho HSchmalz MKeating SLee JMcDonald RWorby NJatowt AMarshall CMilligan I(2017)Information needs for anime recommendationProceedings of the 17th ACM/IEEE Joint Conference on Digital Libraries10.5555/3200334.3200385(305-306)Online publication date: 19-Jun-2017
https://dl.acm.org/doi/10.5555/3200334.3200385
Cho HSchmalz MKeating SLee J(2017)Information Needs for Anime Recommendation: Analyzing Anime Users' Online Forum Queries2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL)10.1109/JCDL.2017.7991602(1-3)Online publication date: Jun-2017
https://doi.org/10.1109/JCDL.2017.7991602

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From Anime To Reality: Embodying An Anime Character As A Humanoid Robot

Anime hair motion design from animation database

User Similarity Adjustment for Improved Recommendations

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