Abstract
A large number of Webtoon contents has caused difficulties on finding relevant Webtoons for users. Thereby, an efficient recommendation services are needed. However, since the existing recommendation method (e.g. collaborative filtering) has two fundamental problems: (i.e., data sparsity and scalability problem), it has difficulties with reflecting users’ personality. In this paper, we propose the MBTI-CF method to solve these problems and to involve users’ personality by building personality-based neighborhood using MBTI. In order to verify the efficiency of the proposed method, we conducted statistical testing by user survey (anonymous users have rated set of the pre-selected Webtoon contents). Three experimental results have shown that MBTI-CF provides improvement in terms of the data sparsity problem and the scalability problem and offers more stable performance.
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Notes
- 1.
Webtoon is also known as web comics, online comics, internet comics.
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Acknowledgments
This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2015-H8501-15-1018) supervised by the IITP (Institute for Information & communications Technology Promotion). Also, this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2014R1A2A2A05007154).
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Yi, MY., Lee, OJ., Jung, J.J. (2016). MBTI-Based Collaborative Recommendation System: A Case Study of Webtoon Contents. In: Vinh, P., Alagar, V. (eds) Context-Aware Systems and Applications. ICCASA 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 165. Springer, Cham. https://doi.org/10.1007/978-3-319-29236-6_11
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