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Movie Recommendation System Using Social Network Analysis and k-Nearest Neighbor

  • Khamphaphone Xinchang
  • Doo-Soon ParkEmail author
  • Phonexay Vilakone
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 536)

Abstract

Many types of research have been conducted in recommendation system to develop approaches to solve the challenges for collaborative filtering problem such as cold start problem; in this paper, we proposed the approach to solving the problem of collaborative by using social network analysis and k-nearest neighbor (k-NN). We used the centrality of social network to detect the community or cluster group to the user, and then apply the k-NN method to find a group for new users with similar personal information such as age, gender, and occupation after that recommendation system will recommend items that users in the group were previously interested for the new.

Keywords

Recommendation system Social network analysis k-Nearest neighbor 

Notes

Acknowledgements

This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under ITRC (Information Technology Research Center) support program (IITP-2018-2014-1-00720) supervised by IITP (Institute for Information & communications Technology Promotion) and the National Research Foundation of Korea (No. NRF-2017R1A2B1008421).

References

  1. 1.
    Park, D.H., Kim, H.K., Choi, I.Y., Kim, J.K.: A review and classification of recommender systems research. In: 2011 International Conference on Social Science and Humanity, IPDR vol. 5 (2011). IACSIT Press, Singapore © (2011)Google Scholar
  2. 2.
    Wang, J., Zhang, N.Y., Yin, J.: Collaborative filtering recommendation based on fuzzy clustering of user preference. In: 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2010) (2010)Google Scholar
  3. 3.
    Ye, F., Zhang, H.: A collaborative filtering recommendation based on users’ interest and correlation of items. In: International Conference on Audio, Language and Image Processing (ICALIP), July 2016Google Scholar
  4. 4.
    Tiwari, S.K., Shivastava, S.K.: An approach for recommender system by combining collaborative filtering with user demographics and items genres. Int. J. Comput. Appl. 128(13), 16–24 (2015). (0975-8887)Google Scholar
  5. 5.
    Yongchang, W., Ligu, Z.: Research on collaborative filtering recommendation algorithm based on mahout. In: 2016 4th International Conference on Applied Computing and Information Technology/3rd International Conference on Computational Science/Intelligence and Applied Informatics/1st International Conference on Big Data, Cloud Computing, Data Science & Engineering. IEEE (2016)Google Scholar
  6. 6.
    Aggarwal, C.C.: Recommender Systems. Springer, Berlin (2016)CrossRefGoogle Scholar
  7. 7.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. PNAS 99(12), 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Miller, B.N., Albert, I., Lam, S.K., et al.: MovieLens unplugged: experiences with an occasionally connected recommender system. In: Proceedings of the 8th International Conference on Intelligent User Interfaces, pp. 263–266. ACM (2003)Google Scholar
  9. 9.
    Parvatikan, S., Joshi, B.: Online book recommendation system by using collaborative filtering and association mining. In: IEEE International Conference on Computational Intelligence and Computing Research (ICCCI), December 2015Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Khamphaphone Xinchang
    • 1
  • Doo-Soon Park
    • 1
    Email author
  • Phonexay Vilakone
    • 1
  1. 1.Computer Science and EngineeringSoonchunhyang UniversityAsanKorea

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