Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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How to improve the diversity on collaborative filtering using tags

  • Received : 2018.05.28
  • Accepted : 2018.07.11
  • Published : 2018.07.31
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Abstract

In this paper, we propose how to improve the lack of diversity in collaborative filtering, using tag scores contained in items rather than ratings of items. Collaborative filtering has excellent performance among recommendation system, but it is evaluated as lacking diversity. In order to solve this problem, this paper proposes a method for supplementing diversity lacking in collaborative filtering by using tags. By using tags that can be used universally without using the characteristics of specific articles in a recommendation system, The proposed method can be used.

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References

  1. Jieun Son, Seoung Bum Kim, Hyunjoong Kim, Sungzoon Cho. "Review and Analysis of Recommender Systems." Journal of the Korean Institute of Industrial Engineers, 41.2 pp. 185-208, April 2015 https://doi.org/10.7232/JKIIE.2015.41.2.185
  2. Jong-Chan Lee, Moon-Ho Lee. "Big data-based information recommendation system." Journal of the Korea Institute of Information and Communication Engineering, 22.3 pp. 443-450. March 2018. https://doi.org/10.6109/JKIICE.2018.22.3.443
  3. Eun-Young Bae, Seok-Jong Yu. "Keyword-based Recommender System Dataset Construction and Analysis". The Journal of Korean Institute of Information Technology, 16(6), pp. 91-99. June 2018
  4. Seok-Jong Yu. "A Study of Improvement of Individual Item Diversity in Collaborative Filtering-based Recommendation." The Journal of Korean Institute of Information Technology, 14.8 pp. 89-94. October 2016
  5. Hyunwoo Kim, Hyoung-Joo Kim. "Item Recommendation using Tag Expansion and Temporal Information." Journal of KIISE : Computing Practices and Letters, 18(7), pp. 521-527. July 2012.
  6. Min Sehee, Yeom Hongseon. "Recipe Recommendation System based on Ingredient By using Hybrid Filtering." Journal of the Korean Information Science Association, 1575-1577. December 2014.
  7. Han Yong Eom, Chang Hyeon Park. "Recommender System based on Hidden Markov Model." Korea Electronic Engineering Conference, 78-81. November 2001.
  8. Nachiketa Sahoo, Param Vir Singh, and Tridas Mukhopadhyay. "A hidden Markov model for collabor ative filtering". MIS Q. 36, 4 1329-1356. December 2012.
  9. Jong-Hun Kim, Kyung-Yong Chung, Joong-Kyung Ryu, Kee-Wook Rim, Jung-Hyun Lee. 2008. A Recommendation Agent System Using HMM-Based Collaborative Filtering in Smart Home Environment. 11-13. Nov 2008.
  10. N. Lathia, S. Hailes, L. Capra, and X. Amatriain, "Temporal Diversity in Recommendation Systems", SIGIR, Geneva, pp. 210-217, July. 2010.
  11. C-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen, "Improving recommendation lists through topic diversification", WWW 2005, Chiba, Japan, pp. 22-32, Jan. 2005.
  12. M. Zhang and N. Hurley, "Avoiding Monotony: Improving the diversity of Recommendation Lists", RecSys 2008, Lausanne, Switzerland, pp. 123-130, Oct. 2008.
  13. T. Zhou, Z. Kuscsik, J-G. Liu, M. Medo, J. R. Wakeling, and Y-C. Zhang, "Solving the apparent diversity-acc uracy dilemma of recommender systems", Proc. of the National Academy of Sciences of the United States of America 107, 10, pp. 4511-4515, Feb. 2010. https://doi.org/10.1073/pnas.1000488107
  14. O. Celma and P. Herrera, "A New Approach to Evaluating Novel Recommendations", RecSys 2008, Lausanne, Switzerland, pp. 179-186, Oct. 2008.
  15. G. Adomavicius and Y. Kwon, "Improving Aggregate Recommendation Diversity Using Ranking-based Techniques", IEEE Transactions on Knowledge and Data Engineering. Vol. 24, No. 5, pp. 896-911, May 2012. https://doi.org/10.1109/TKDE.2011.15
  16. Seok-Jong Yu."Frequency-sensitive Diversification in Collaborative Filtering". Journal of the Korea Information Technology Association, 13(7), 93-98. July 2015.