Journal of KIISE (정보과학회 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지
DOI QR코드

DOI QR Code

Enhancing the Performance of Recommender Systems Using Online Review Clusters

온라인 리뷰 클러스터를 이용한 추천 시스템 성능 향상

  • 노기섭 (공군사관학교 전산정보학과) ;
  • 오하영 (아주대학교 다산학부대학) ;
  • 이재훈 (서울대학교 컴퓨터공학부)
  • Received : 2017.07.13
  • Accepted : 2017.11.29
  • Published : 2018.02.15
⟨ Previous Next ⟩

Abstract

The recommender system (RS) has emerged as a solution to overcome the constraints of excessive information provision and to maximize profit and reputation for information providers. Although the RS can be implemented with various approaches, there is no study on how to appropriately utilize the information generated from the review of the recommended object. We propose a method to improve the performance of RS by using cluster information generated from online review. We implemented the proposed method and experimented with real data, and confirmed that the performance is significantly improved compared to the existing approaches.

추천 시스템은 과도한 정보제공으로 인한 정보 수용자의 결정 제약을 극복하고, 정보 제공자에게는 이윤과 평판을 최대화 시킬 수 있는 해결책으로 등장하였다. 추천 시스템은 다양한 접근법으로 구현이 가능하지만, 추천 대상 객체의 리뷰에서 생성되는 다양한 소셜 정보를 적절히 활용하는 방안은 연구되지 못하였다. 본 논문에서는 기존의 접근법과는 다르게 온라인 리뷰에서 생성되는 클러스터 정보를 이용하여 추천 시스템의 성능을 향상시키는 방식을 제안하였다. 제안하는 방식을 구현하고 실제 데이터를 활용하여 실험한 결과 기존의 방식들보다 성능이 월등히 향상됨을 확인하였다.

Keywords

Acknowledgement

Supported by : 한국연구재단

References

  1. B. Xiao and I. Benbasat, "E-commerce product recommendation agents: use, characteristics, and impact," MIS quarterly, Vol. 31, pp. 137-209, 2007. https://doi.org/10.2307/25148784
  2. P. Melville, R. J. Mooney, and R. Nagarajan, "Content-boosted collaborative filtering for improved recommendations," Aaai/iaai, 2002, pp. 187-192.
  3. R. J. Mooney and L. Roy, "Content-based book recommending using learning for text categorization," Proc. of the fifth ACM conference on Digital libraries, pp. 195-204, 2000.
  4. R. Pan, Y. Zhou, B. Cao, N. N. Liu, R. Lukose, M. Scholz, et al., "One-class collaborative filtering," Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on, pp. 502-511, 2008.
  5. X. Su and T. M. Khoshgoftaar, "A survey of collaborative filtering techniques," Advances in artificial intelligence, Vol. 2009, p. 4, 2009.
  6. Q. Li and B. M. Kim, "Clustering approach for hybrid recommender system," Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference on, pp. 33-38, 2003.
  7. W. Woerndl, C. Schueller, and R. Wojtech, "A hybrid recommender system for context-aware recommendations of mobile applications," Data Engineering Workshop, 2007 IEEE 23rd International Conference on, pp. 871-878, 2007.
  8. A. Anderson, D. Huttenlocher, J. Kleinberg, and J. Leskovec, "Effects of user similarity in social media," Proc. of the fifth ACM international conference on Web search and data mining, pp. 703-712, 2012.
  9. D. Crandall, D. Cosley, D. Huttenlocher, J. Kleinberg, and S. Suri, "Feedback effects between similarity and social influence in online communities," Proc. of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 160-168, 2008.
  10. G.-N. Hu, X.-Y. Dai, Y. Song, S. Huang, and J. Chen, "A Synthetic Approach for Recommendation: Combining Ratings, Social Relations, and Reviews," IJCAI, pp. 1756-1762, 2015.
  11. R. Forsati, I. Barjasteh, F. Masrour, A.-H. Esfahanian, and H. Radha, "Pushtrust: An efficient recommendation algorithm by leveraging trust and distrust relations," Proc. of the 9th ACM Conference on Recommender Systems, pp. 51-58, 2015.
  12. C. Cai, R. He, and J. McAuley, "SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation."
  13. G. Noh, Y.-m. Kang, H. Oh, and C.-k. Kim, "Robust Sybil attack defense with information level in online Recommender Systems," Expert Systems with Applications, Vol. 41, pp. 1781-1791, 2014/03/01/2014. https://doi.org/10.1016/j.eswa.2013.08.077
  14. H. Ma, I. King, and M. R. Lyu, "Effective missing data prediction for collaborative filtering," Proc. of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 39-46, 2007.
  15. I. Guy, N. Zwerdling, D. Carmel, I. Ronen, E. Uziel, S. Yogev, et al., "Personalized recommendation of social software items based on social relations," Proc. of the third ACM conference on Recommender systems, pp. 53-60, 2009.
  16. M. Jamali and M. Ester, "A matrix factorization technique with trust propagation for recommendation in social networks," Proc. of the fourth ACM conference on Recommender systems, pp. 135-142, 2010.
  17. H. Ma, H. Yang, M. R. Lyu, and I. King, "Sorec: social recommendation using probabilistic matrix factorization," Proc. of the 17th ACM conference on Information and knowledge management, pp. 931-940, 2008.
  18. K. Yoshii, M. Goto, K. Komatani, T. Ogata, and H. G. Okuno, "An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model," IEEE Transactions on Audio, Speech, and Language Processing, vol. 16, pp. 435-447, 2008. https://doi.org/10.1109/TASL.2007.911503
  19. Y. Zhang and D.-Y. Yeung, "Overlapping community detection via bounded nonnegative matrix tri-factorization," Proc. of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 606-614, 2012.
  20. H. Ma, D. Zhou, C. Liu, M. R. Lyu, and I. King, "Recommender systems with social regularization," Proc. of the fourth ACM international conference on Web search and data mining, pp. 287-296, 2011.