Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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A New Collaborative Filtering Method for Movie Recommendation Using Genre Interest

영화 추천을 위한 장르 흥미도를 이용한 새로운 협력 필터링 방식

  • Lee, Soojung (Dept. of Computer Education, Gyeongin National Univ. of Education)
  • 이수정 (경인교대 컴퓨터교육과)
  • Received : 2014.05.16
  • Accepted : 2014.08.20
  • Published : 2014.08.28
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Abstract

Collaborative filtering has been popular in commercial recommender systems, as it successfully implements social behavior of customers by suggesting items that might fit to the interests of a user. So far, most common method to find proper items for recommendation is by searching for similar users and consulting their ratings. This paper suggests a new similarity measure for movie recommendation that is based on genre interest, instead of differences between ratings made by two users as in previous similarity measures. From extensive experiments, the proposed measure is proved to perform significantly better than classic similarity measures in terms of both prediction and recommendation qualities.

협력 필터링은 상업적 추천 시스템에서 널리 사용되어 왔는데, 고객의 사회적 행태를 구현하여 사용자의 흥미에 부합하는 항목들을 제안하기 때문이다. 현재까지 적절한 항목을 추천하기 위한 가장 보편적인 방법은 유사한 사용자들을 찾아 그들의 평가치를 참조하는 방법이다. 본 논문은 영화를 추천하기 위해서 장르 흥미도를 기반으로 하는 새로운 유사도 공식을 제안하는데, 이는 기존 공식에서 사용자들의 평가등급 차이를 기반으로 하는 것과 대비된다. 광범위한 실험 결과에 따르면, 제안한 공식은 정확도와 추천의 질에 있어서 전통적인 유사도 공식의 성능을 크게 향상시키는 것으로 확인되었다.

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References

  1. G. Adomavicius, & A. Tuzhilin, Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge & Data Engineering, IEEE, Vol. 17, No. 6, pp. 734-749, 2005. https://doi.org/10.1109/TKDE.2005.99
  2. S. Han, S. Chee, J. Han, & K. Wang, RecTree: An efficient collaborative filtering method, The 3rd International Conference on Data Warehousing and Knowledge Discovery, pp. 141-151, 2001.
  3. X. Su, & T. M. Khoshgoftaar, Collaborative filtering for multi-class data using belief nets algorithms, The 13th International Conference on Tools with Artificial Intelligence, IEEE, pp. 497-504, 2006.
  4. R. Greiner, X. Su, B. Shen, & W. Zhou, Structural extension to logistic regression: Discriminative parameter learning of belief net classifiers, The Eighteenth Annual National Conference on Artificial Intelligence, pp. 167-173, 2002.
  5. T. Hofmann, Latent semantic models for collaborative filtering, ACM Transactions on Information Systems, ACM, Vol. 22, No. 1, pp. 89-115, 2004. https://doi.org/10.1145/963770.963774
  6. D. Anand, & K. K. Bharadwaj, Adaptive user similarity measures for recommender systems: A genetic programming approach, The 3rd IEEE International Conference on Computer Science and Information Technology, pp. 121-125, 2010.
  7. J. Bobadilla, F. Ortega, A. Hernando, & J. Alcala, Improving collaborative filtering recommender system results and performance using genetic algorithms, Knowledge-Based Systems, Elsevier Science Inc., Vol. 24, No. 8, pp. 1310-1316, 2011. https://doi.org/10.1016/j.knosys.2011.06.005
  8. C. Baoxian, M. Fei, & L. Sujuan, A collaborative filtering recommendation algorithm based on user topic preference, International Journal of Advancements in Computing Technology, AICIT, Vol. 4, No. 14, pp. 342-351, 2012. https://doi.org/10.4156/ijact.vol4.issue14.39
  9. J. Bobadilla, A. Hernando, F. Ortega, & A. Gutierrez, Collaborative filtering based on significances, Information Sciences, Elsevier Science Inc., Vol. 185, No. 1, pp. 1-17, 2012. https://doi.org/10.1016/j.ins.2011.09.014
  10. X. Tang, W. Deng, & J. Liu, A personalized recommendation method based on comprehensive interest, International Journal of Advancements in Computing Technology, AICIT, Vol. 5, No. 5, pp. 157-164, 2013.
  11. H. J. Ahn, A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem, Information Sciences, Elsevier Science Inc., Vol. 178, No. 1, pp. 37-51, 2008. https://doi.org/10.1016/j.ins.2007.07.024
  12. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, & J. Riedl, GroupLens: An open architecture for collaborative filtering of netnews, Proceedings of the ACM Conference on Computer Supported Cooperative Work, pp. 175-186, 1994.
  13. J. Bobadilla, F. Serradilla, & J. Bernal, A new collaborative filtering metric that improves the behavior of recommender systems, Knowledge-Based Systems, Elsevier Science Inc., Vol. 23, No. 6, pp. 520-528, 2010. https://doi.org/10.1016/j.knosys.2010.03.009
  14. G. Koutrica, B. Bercovitz, & H. Garcia-Molina, FlexRecs: Expresing and combining flexible recommendations, Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, ACM, pp. 745-758, 2009.
  15. J. L. Herlocker, J. A. Konstan, L. G. Terveen, & J. T. Riedl, Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems, ACM, Vol. 22, No. 1, pp. 5-53, 2004. https://doi.org/10.1145/963770.963772
  16. M. Gao, Z. Wu, & F. Jiang, Userrank for item-based collaborative filtering recommendation, Information Processing Letters, Elsevier Science Inc., Vol. 111, No. 9, pp. 440-446, 2011. https://doi.org/10.1016/j.ipl.2011.02.003