International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Improved Post-Filtering Method Using Context Compensation

  • Kim, Be-Deu-Ro (Department of Electrical and Computer Engineering, Sungkyunkwan University) ;
  • Lee, Jee-Hyong (Department of Electrical and Computer Engineering, Sungkyunkwan University)
  • Received : 2016.06.10
  • Accepted : 2016.06.20
  • Published : 2016.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

According to the expansion of smartphone penetration and development of wearable device, personal context information can be easily collected. To use this information, the context aware recommender system has been actively studied. The key issue in this field is how to deal with the context information, as users are influenced by different contexts while rating items. But measuring the similarity among contexts is not a trivial task. To solve this problem, we propose context aware post-filtering to apply the context compensation. To be specific, we calculate the compensation for different context information by measuring their average. After reflecting the compensation of the rating data, the mechanism recommends the items to the user. Based on the item recommendation list, we recover the rating score considering the context information. To verify the effectiveness of the proposed method, we use the real movie rating dataset. Experimental evaluation shows that our proposed method outperforms several state-of-the-art approaches.

Keywords

References

  1. D. H. Kim, J. M. Shin, and S. W. Park, "Decision tree based application recommendation system," Proceedings of Korea Computer Congress, vol. 39, no. 1, pp. 140-142, 2012.
  2. G. Adomavicius, R. Sankaranarayanan, S. Sen, and A. Tuzhilin, "Incorporating contextual information in recommender systems using a multidimensional approach," ACM Transactions on Information Systems, vol. 23, no. 1, pp. 103-145, 2005. http://dx.doi.org/10.1145/1055709.1055714
  3. K. C. Choi, J. H. Yang, and H. J. Lee, "Context-aware recommender system using purchase history data," Proceedings of Korean Institute of Information Scientists and Engineers, Pyeongchang, Korea, 2014, pp. 595-597.
  4. G. Adomavicius and A. Tuzhilin, "Toward the next generation of recommender systems: a survey of the stateof-the-art and possible extensions," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734-749, 2005. http://dx.doi.org/10.1109/TKDE.2005.99
  5. M. Unger, A. Bar, B. Shapira, and L. Rokach, "Towards latent context-aware recommendation systems," Knowledge-Based Systems, vol. 104, pp. 165-178, 2016. http://dx.doi.org/10.1016/j.knosys.2016.04.020
  6. V. Codina, F. Ricci, and L. Ceccaroni, "Distributional semantic pre-filtering in context-aware recommender systems," User Modeling and User-Adapted Interaction, vol. 26, no. 1, pp. 1-32, 2016. http://dx.doi.org/10.1007/s11257-015-9158-2
  7. K. Verbert, N. Manouselis, X. Ochoa, M. Wolpers, H. Drachsler, I. Bosnic, and E. Duval, "Context-aware recommender systems for learning: a survey and future challenges," IEEE Transactions on Learning Technologies, vol. 5, no. 4, pp. 318-335, 2012. http://dx.doi.org/10.1109/TLT.2012.11
  8. H. Zhu, E. Chen, H. Xiong, K. Yu, H. Cao, and J. Tian, "Mining mobile user preferences for personalized contextaware recommendation," ACM Transactions on Intelligent Systems and Technology, vol. 5, no. 4, article no. 58, 2015. http://dx.doi.org/10.1145/2532515
  9. X. Ramirez-Garcia and M. Garcia-Valdez, "Post-filtering for a restaurant context-aware recommender system," in Recent Advances on Hybrid Approaches for Designing Intelligent Systems, O. Castillo, P. Melin, W. Pedrycz, and J. Kacprzyk, Eds. Cham: Springer International Publishing, 2014, pp. 695-707. http://dx.doi.org/10.1007/978-3-319-05170-3 49
  10. L. Baltrunas, "Context-aware collaborative filtering recommender systems," Ph.D. dissertation, Free University of Bozen-Bolzano, Bolzano, Italy, 2011.
  11. A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver, "Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering," in Proceedings of the 4th ACM Conference on Recommender Systems, Barcelona, Spain, 2010, pp. 79-86. http://dx.doi.org/10.1145/1864708.1864727
  12. Y. Shi, M. Larson, and A. Hanjalic, "Mining moodspecific movie similarity with matrix factorization for context-aware recommendation," in Proceedings of the Workshop on Context-Aware Movie Recommendation, Barcelona, Spain, 2010, pp. 34-40. http://dx.doi.org/10.1145/1869652.1869658
  13. B. N. Schilit and M. M. Theimer, "Disseminating active map information to mobile hosts," IEEE Network, vol. 8, no. 5, pp. 22-32, 1994. http://dx.doi.org/10.1109/65.313011
  14. G. D. Abowd, A. K. Dey, P. J. Brown, N. Davies, M. Smith, and P. Steggles, "Towards a better understanding of context and context-awareness," in Proceedings of 1st International Symposium on Handheld and Ubiquitous Computing, Karlsruhe, Germany, 1999, pp. 304-307. http://dx.doi.org/10.1007/3-540-48157-5 29
  15. G. Adomavicius and A. Tuzhilin, "Context-aware recommender systems," in Proceedings of the 2008 ACM Conference on Recommender Systems, Lousanne, Switzerland, 2008, pp. 335-336. http://dx.doi.org/10.1145/1454008.1454068
  16. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Itembased collaborative filtering recommendation algorithms," in Proceedings of the 10th International Conference on World Wide Web, Hong Kong, 2001, pp. 285-295. http://dx.doi.org/10.1145/371920.372071
  17. Y. Zheng, B. Mobasher, and R. Burke, "CARSKit: a Java-based context-aware recommendation engine," in Proceedings of 2015 IEEE International Conference on Data Mining Workshop, Atlantic City, NJ, 2015, pp. 1668-1671. http://dx.doi.org/10.1109/ICDMW.2015.222
  18. D. Lemire and A. Maclachlan, "Slope one predictors for online rating-based collaborative filtering," in Proceedings of the 2005 SIAM International Conference on Data Mining, Newport Beach, CA, 2005, pp. 471-475. http://dx.doi.org/10.1137/1.9781611972757.43
  19. L. Baltrunas and F. Ricci, "Context-based splitting of item ratings in collaborative filtering," in Proceedings of the 3rd ACM Conference on Recommender Systems, New York, NY, 2009, pp. 245-248. http://dx.doi.org/10.1145/1639714.1639759
  20. G. Shani and A. Gunawardana, "Evaluating recommendation systems," in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds. New York, NY: Springer, 2011, pp. 257-297. http://dx.doi.org/10.1007/978-0-387-85820-3_8