Skip to main content
SpringerLink
Log in

An algorithm for movie classification and recommendation using genre correlation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Collaborative filtering (CF), a technique used by recommendation systems, predicts and recommends items (information, products or services) that the user might like. Amazon.com’s recommender system is one of the most famous examples of CF. Recommendation systems are popular in both commercial and research sectors, and they are applied in a variety of applications such as movies, music, books, social connections and venues. In particular, movie recommendation systems produce personal recommendations for movies. Existing CF algorithms employed in movie recommendation systems predict the unknown rating of a given user for a movie using only the ratings (i.e., preferences) of other like-minded users who have seen the movie. In such approaches, there exist certain limits in improving the accuracy of recommendation systems. This paper proposes an algorithm for movie recommendation that exploits the genre of the movie to enhance the accuracy of rating predictions. The proposed algorithm 1) numerically measures the correlation between movie genres using movie rating information; 2) classifies movies using the genre correlations and generates a list of recommended movies for the target user with the classified movies; and finally 3) predicts the ratings of the movies in the list using traditional CF algorithms. The experimental results show that the proposed algorithm yields higher accuracy in movie rating predictions than existing movie recommendation algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Bell RM, Koren Y, Volinsky C (2008) The Bellkor 2008 solution to the Netflix prize. Stat Res Dep AT&T Res

  2. Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. Proc 40th Conf Uncertain AI 1:43–52

  3. Cacheda F, Carneiro V, Fernández D, Formoso V (2011) Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans Web 5:1–33. doi:10.1145/1921593

    Article  Google Scholar 

  4. Choi SM, Ko SK, Han YS (2012) A movie recommendation algorithm based on genre correlations. Expert Syst Appl 39:8079–8085. doi:10.1016/j.eswa.2012.01.132

    Article  Google Scholar 

  5. Ding Y, Li X (2005) Time weight collaborative filtering. Proc 14th ACM Int Conf Inf Knowl Manag. doi:10.1145/1099554.1099689

  6. Edson B, Santos Jr, Rudinei Goularte, Marcelo G, Manzato G (2014) Personalized collaborative filtering: a neighborhood model based on contextual constraints. Proc 29th Annu ACM Symp Appl Comput 1:919–924. doi:10.1145/2554850.2555017

  7. Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22:5–53. doi:10.1145/963770.963772

    Article  Google Scholar 

  8. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42:30–37. doi:10.1109/MC.2009.263

    Article  Google Scholar 

  9. Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. Internet Comput IEEE 7:76–80. doi:10.1109/MIC.2003.1167344

    Article  Google Scholar 

  10. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. Proc 10th Int Conf WWW 1:285–295. doi:10.1145/371920.372071

  11. Soares M, Viana P (2014) Tuning metadata for better movie content-based recommendation systems. Multimed Tools Appl. doi:10.1007/s11042-014-1950-1

    Google Scholar 

  12. Wu IC, Niu YF (2013) Integrating the anchoring process with preference stability for interactive movie recommendations. In: Yamamoto S (ed) Human interface and the management of information. Springer, Berlin, pp 639–648

    Google Scholar 

  13. Zheng Q, Horace HSIP (2012) Customizable surprising recommendation based on the tradeoff between genre difference and genre similarity. Web Intell Intell Agent Technol 1:702–709. doi:10.1109/WI-IAT.2012.70

    Google Scholar 

Download references

Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2015R1D1A1A01059937).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Chung-Ang University, Dongjak-gu, Seoul, 156-756, Republic of Korea

    Tae-Gyu Hwang, Chan-Soo Park, Jeong-Hwa Hong & Sung Kwon Kim

Authors
  1. Tae-Gyu Hwang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chan-Soo Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jeong-Hwa Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sung Kwon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sung Kwon Kim.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hwang, TG., Park, CS., Hong, JH. et al. An algorithm for movie classification and recommendation using genre correlation. Multimed Tools Appl 75, 12843–12858 (2016). https://doi.org/10.1007/s11042-016-3526-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3526-8

Keywords

Search

Navigation