Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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A Study on the Real-Time Preference Prediction for Personalized Recommendation on the Mobile Device

모바일 기기에서 개인화 추천을 위한 실시간 선호도 예측 방법에 대한 연구

  • Lee, Hak Min (Digital Innovation Team, Shinhan Data System) ;
  • Um, Jong Seok (Division of Computer Engineering, Hansung University)
  • Received : 2017.01.13
  • Accepted : 2017.01.31
  • Published : 2017.02.28
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Abstract

We propose a real time personalized recommendation algorithm on the mobile device. We use a unified collaborative filtering with reduced data. We use Fuzzy C-means clustering to obtain the reduced data and Konohen SOM is applied to get initial values of the cluster centers. The proposed algorithm overcomes data sparsity since it extends data to the similar users and similar items. Also, it enables real time service on the mobile device since it reduces computing time by data clustering. Applying the suggested algorithm to the MovieLens data, we show that the suggested algorithm has reasonable performance in comparison with collaborative filtering. We developed Android-based smart-phone application, which recommends restaurants with coupons and restaurant information.

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References

  1. J. Bobadilla, F. Ortega, A. Hernando, and A. Gutierrez, "Recommender Systems Survey," Knowledge-Based Systems, Vol. 46, pp. 109- 132, 2013. https://doi.org/10.1016/j.knosys.2013.03.012
  2. J.I. Kim, "A Recommendation Method of Similar Clothes on Intelligent Fashion Coordination System," Journal of Korea Multimedia Society, Vol. 12, No. 5, pp. 688-698, 2009.
  3. M. Eirnaki and M. Vazirgiannis, "Web Mining for Web Personalization," ACM Transactions on Internet Technology, Vol. 3, No. 1, pp. 1-27, 2003. https://doi.org/10.1145/643477.643478
  4. G. Linden, B. Smith, and J. York, "Amazon.com Recommendations: Item-to-Item Collaborative Filtering," IEEE Internet Computing, Vol. 7, No. 1, pp. 76-80, 2003. https://doi.org/10.1109/MIC.2003.1167344
  5. S. Breese, D. Heckerman, and C. Kadie, "Empirical Analysis of Predictive Algorithms for Collaborative Filtering," Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp.43-52, 1998.
  6. L.M. Campos, J.M. Fernandez-Luna, J.F. Huete, and M.A. Rueda-Morales, "Combining Content-based and Collaborative Recommendations: A Hybrid Approach Based on Bayesian Networks," International Journal of Approximate Reasoning, Vol. 51, Issue 7, pp. 785-799, 2010. https://doi.org/10.1016/j.ijar.2010.04.001
  7. T. Hofmann, "Latent Semantic Model for Collaborative Filtering," ACM Transactions on Information Systems, Vol. 22, No. 1, pp. 89-115, 2004. https://doi.org/10.1145/963770.963774
  8. J.L. Herlocker, J.A. Konstan, A. Borchers, and J. Riedl "An Algorithmic Framework for Performing Collaborative Filtering," Proceeding of Special Interest Group on Information Retrieval , pp. 230-237, 1999.
  9. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-based Collaborative Filtering Recommendation Algorithms," Proceedings of the 10th international Conference on World Wide Web , pp. 285-295, 2001.
  10. K. Zagoris, N. Papamarkos, and I. Koustoudis, "Color Reduction Using the Combination of the Kohonen Self-Organized Feature Map and the Gustafson-Kessel Fuzzy Algorithm," Machine Learning and Data Mining in Pattern Recognition, LNAI 4571, pp. 703-715, 2007.
  11. J. Wang, A.P. de Vries, and J.T. Reinders, "Unifying Userbased and Itembased Collaborative Filtering Approaches by Similarity Fusion," Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 6-11, 2006.
  12. MovieLens Dataset, Social Computing Research at the University of Minnesota. http://www.grouplens.org/ (accessed Aug., 20, 2015).