MBTI-Based Collaborative Recommendation System: A Case Study of Webtoon Contents

  • Myeong-Yeon Yi
  • O-Joun LeeEmail author
  • Jason J. Jung
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 165)


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.


Webtoon Recommendation MBTI (Myers-Briggs Type Indicator) Collaborative filtering 



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).


  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 1–19 (2009). Article ID 421425, HindawiCrossRefGoogle Scholar
  3. 3.
    Billsus, D., Pazzani, M.J.: Learning collaborative information filters. In: Proceedings of the 15th International Conference on Machine Learning, vol. 98, pp. 46–54 (1998)Google Scholar
  4. 4.
    Tkalčič, M., Kunaver, M., Tasič, J., Košir, A.: Personality based user similarity measure for a collaborative recommender system. In: Proceedings of the 5th Workshop on Emotion in Human-Computer Interaction-Real World Challenges, pp. 30–37 (2009)Google Scholar
  5. 5.
    Gosling, S.D., Rentfrow, P.J., Swann Jr., W.B.: A very brief measure of the Big-Five personality domains. J. Res. Pers. 37(6), 504–528 (2003)CrossRefGoogle Scholar
  6. 6.
    Harasym, P.H., Leong, E.J., Juschka, B.B., Lucier, G.E., Lorcheider, F.L.: Myers-briggs psychological type and achievement in anatomy and physhiology. Am. J. Physiol. 268(6 pt. 3), S61–S65 (1995)Google Scholar
  7. 7.
    Burger, J.M.: Personality, 7th edn. Thomson/Wadsworth, Belmont (2008)Google Scholar
  8. 8.
    Kim, H.-G., Namgoong, H., Eune, J., Song, M.: A proposed movie recommendation method using emotional word selection. In: Ozok, A., Zaphiris, P. (eds.) OCSC 2009. LNCS, vol. 5621, pp. 525–534. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Rentfrow, P.J., Gosling, S.D.: The do re mi’s of everyday life: the structure and personality correlates of music preferences. J. Pers. Soc. Psychol. 84(6), 1236–1256 (2003)CrossRefGoogle Scholar
  10. 10.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRefGoogle Scholar
  11. 11.
    Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5–6), 393–408 (1999)CrossRefGoogle Scholar
  13. 13.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)Google Scholar
  14. 14.
  15. 15.
    Lee, O.-J., Jung, J.J., You, E.-S.: Predictive clustering for performance stability in collaborative filtering techniques. In: Proceedings of 2nd IEEE International Conference on Cybernetics, CYBCONF 2015, Gdynia, Poland, pp. 24–26 (2015)Google Scholar
  16. 16.
    Rousidis, I., Plexousakis, D., Theoharopoulos, E., Papagelis, M.: Incremental collaborative filtering for highly-scalable recommendation algorithms. In: Hacid, M.-S., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds.) ISMIS 2005. LNCS (LNAI), vol. 3488, pp. 553–561. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  17. 17.
    Hu, R., Pu, P.: Enhancing collaborative filtering systems with personality information. In: RecSys 2011, Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 197–204 (2011)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringChung-Ang UniversitySeoulSouth Korea

Personalised recommendations