Journal of KIISE (정보과학회 논문지)

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

DOI QR Code

A Group Modeling Strategy Considering Deviation of the User's Preference in Group Recommendation

그룹 추천에서 사용자 선호도의 편차를 고려한 그룹 모델링 전략

  • Received : 2016.06.23
  • Accepted : 2016.07.14
  • Published : 2016.10.15
⟨ Previous Next ⟩

Abstract

Group recommendation analyzes the characteristics and tendency of a group rather than an individual and provides relevant information for the members of the group. Existing group recommendation methods merely consider the average and frequency of a preference. However, if the users' preferences have large deviations, it is difficult to provide satisfactory results for all users in the group, although the average and frequency values are high. To solve these problems, we propose a method that considers not only the average of a preference but also the deviation. The proposed method provides recommendations with high average values and low deviations for the preference, so it reflects the tendency of all group members better than existing group recommendation methods. Through a comparative experiment, we prove that the proposed method has better performance than existing methods, and verify that it has high performance in groups with a large number of members as well as in small groups.

그룹 추천은 개인이 아닌 그룹의 특성 및 성향을 분석하여 구성원들에게 적합한 정보를 제공하는 추천 방식이다. 기존의 그룹 추천 방식은 평균 선호도나 선호 횟수에 기반한 그룹 모델링 전략을 사용한다. 하지만 평균이 높고 선호 횟수가 많은 관심사더라도 선호도의 편차가 크다면, 그룹 내 구성원 모두를 만족시키는 추천 결과를 제공하기가 어렵다. 본 논문에서는 이를 개선하고자 관심사에 대한 평균 선호도에 선호도 편차를 가중치로 하는 그룹 모델링 전략을 제안한다. 제안하는 방법은 평균 선호도가 높으면서 선호도 편차가 작은 관심사들을 추천 결과로 제공해줌으로써 기존의 그룹 모델링 전략보다 더 많은 그룹 내 구성원들을 만족시키는 정보를 제공하는 것이 가능하다. 실험을 통해 제안하는 그룹 모델링 전략이 기존의 방식에 비해 높은 성능을 보였고, 소규모의 사용자뿐만 아니라 많은 수의 사용자가 형성하는 그룹에서도 높은 성능을 가짐을 확인하였다.

Keywords

Acknowledgement

Supported by : 한국연구재단

References

  1. J. Bobadilla, F. Ortega, A. Hernando, and A. Gutierrez, "Recommender systems survey," Journal of Knowledge-Based Systems, Vol. 46, pp. 109-132, 2013. https://doi.org/10.1016/j.knosys.2013.03.012
  2. M. Kompan and M. Bielikova, "Group recommendations: survey and perspectives," Journal of Computing and Informatics, Vol. 33, pp. 446-476, 2014.
  3. M. O'Connor, D. Cosley, J. Konstan, and J. Riedl, "PolyLens: A Recommender System for Groups of Users," Proc. of European Conference on Computer-Supported Cooperative Work, pp. 199-218, 2001.
  4. J. McCarthy, and T. Anagnost, "MusicFX: An arbiter of group preferences for computer supported collaborative workouts," Proc. of Computer Supported Cooperative Work, pp. 363-372, 1998. pp. 765-774, 2011.
  5. L. Ardissono, A. Goy, G. Petrone, M. Segnan, and P. Torasso, "Tailoring the Recommendation of Tourist Information to Heterogeneous User Groups," Proc. of International Workshops OHS-7, SC-3, and AH-3, LNCS 2266, pp. 280-295, 2002.
  6. L. Boratto, S. Carta, and G. Fenu, "Discovery and representation of the preferences of automatically detected groups: Exploiting the link between group modeling and clustering," Journal of Future Generation Computer Systems, 2015.
  7. A. Jameson, and B. Smyth, "Recommendation to groups," Journal of The adaptive web, pp. 596-627, 2007.
  8. J. Masthoff, "Group modeling: Selecting a sequence of television items to suit a group of viewers," Journal of User Modeling and User-Adapted Interaction, Vol. 14 pp. 37-85, 2004. https://doi.org/10.1023/B:USER.0000010138.79319.fd
  9. H. Lieberman, N.W.V. Dyke, and A.S. Vivacqua, "Let's browse: A collaborative web browsing agent," Proc. of Intelligent User Interfaces, pp. 65-68, 1999.
  10. S. Pizzutilo, B. De Carolis, G. Cozzolongo, and F. Ambruoso, "Group modeling in a public space: methods, techniques, experiences," Proc. of World Scientific and Engineering Academy and Society, pp. 175-180, 2005.
  11. K. McCarthy, M. Salam'o, L. Coyle, L. McGinty, B. Smyth, and P. Nixon, "CATS: A Synchronous Approach to Collaborative Group Recommendation," Proc. of FLAIRS Conference, pp. 86-91, 2006.
  12. A. Crossen, J. Budzik, and K. J. Hammond, "Flytrap: intelligent group music recommendation," Proc. of Intelligent User Interfaces, pp. 184-185, 2002.
  13. Z. Yu, X. Zhou, Y. Hao, and J. Gu, "Tv program recommendation for multiple viewers based on user profile merging," Journal of User Modeling and User-Adapted Interaction, Vol. 16, pp. 63-82, 2006. https://doi.org/10.1007/s11257-006-9005-6
  14. Y.-L. Chen, L.-C. Cheng, and C.-N. Chuang, "A group recommendation system with consideration of interactions among group members," Journal of Expert Systems with Applications, Vol. 34, pp. 2082-2090, 2008. https://doi.org/10.1016/j.eswa.2007.02.008
  15. J. Wang, Z. Liu, and H. Zhao, "Group Recommendation Using Topic Identification in Social Networks," Proc. of Intelligent Human-Machine Systems and Cybernetics, pp. 355-358, 2014.
  16. L. Baltrunas, T. Makcinskas, and F. Ricci, "Group recommendations with rank aggregation and collaborative filtering," Proc. of Recommender System, pp. 119-126, 2010.
  17. J. Bobadilla, F. Serradilla, and J. Bernal, "A new collaborative filtering metric that improves the behavior of recommender systems," Journal of Knowledge Based Systems, Vol. 23, pp. 520-528, 2010. https://doi.org/10.1016/j.knosys.2010.03.009