The relation between user intervention and user satisfaction for information recommendation

Proceedings of the ACM Symposium on Applied Computing

Volumn , Issue , 2012, Pages 2002-2007

  Hijikata, Yoshinori ^a   Kai, Yuki ^a   Nishida, Shogo ^a  

^a OSAKA UNIVERSITY   (Japan)

Author keywords

content based filtering; recommender system; user context; user intervention; user profile; user satisfaction

Indexed keywords

CONTENT BASED FILTERING; USER CONTEXT; USER INTERVENTION; USER PROFILE; USER SATISFACTION;

COMPUTATION THEORY;

RECOMMENDER SYSTEMS;

EID: 84863577221     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2245276.2232109     Document Type: Conference Paper

References (20)

1. Herlocker, J.L., et al.: Evaluating Collaborative Filtering Recommender Systems, ACM Trans. on Information Systems, Vol. 22, Issue 1, pp. 5-53 (2004)
2. McNee, S.M., et al.: Being Accurate is Not Enough: How Accuracy Metrics have hurt Recommender Systems, Proc. of CHI'06, pp. 1097-1101 (2006)
3. Swearingen, K. and Sinha, R.: Beyond algorithms: An HCI perspective on recommender systems, Proc. of SIGIR Workshop on Recommender Systems (2001)
4. Ziegler, C.-N., et al.: Improving Recommendation Lists Through Topic Diversification, Proc. of WWW'05, pp. 22-32 (2005)
5. Terveen L., et al.: Specifying Preferences Based on User History, Proc. of CHI'02, pp. 315-322 (2002)
6. Waern, A.: User Involvement in Automatic Filtering, User Modeling and User-Adapted Interaction, Vol. 14, pp. 201-237 (2004)
7. Hijikata, Y., et al.: Content-based Music Filtering System with Editable User Profile, Proc. of ACM SAC'06, pp.1050-1057 (2006)
8. Ahn, J., et al.: Open User Profiles for Adaptive News Systems: Help or Harm?, Proc. of WWW'07, pp. 11-20 (2007)
9. Swearingen, K. and Sinha, R.: Beyond algorithms: A Human-Centered Evaluation of Recommender Systems, SIMS 213, UC Berkeley (2002)
10. Financial Times "Win the Hearts of Bachelors: Marriage Meeting Sites Battle in the USA"http://news.goo.ne.jp/article/ft/life/science/ft- 20061226-01.html (2006)
11. Hijikata, Y.: User Profiling Technique for Information Recommendation and Information Filtering, Journal of JSAI, Vol. 19, No. 3, pp. 365-372 (2004)
12. Adomavicius, G., et al.: Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach, ACM Trans. on Information Systems, Vol. 23, No. 1, pp. 103-145 (2005)
13. Ono, C., et al.: A Context-Aware Movie Preference Model Using a Bayesian Network for Recommendation and Promotion, Proc. of User Modeling 2007, LNCS Vol. 4511, pp. 257-266 (2007)
14. Oku, K., et al.: A Recommendation System Considering Past / Current / Future Contexts, Proc. of Workshop on Context-Aware Recommender Systems (2010)
15. Condliff, M.K., et al.: Bayesian Mixed-Effects Models for Recommender Systems, Proc. of ACM SIGIR Workshop on Recommender Systems (1999)
16. Zhang, Y. and Koren, J.: Efficient Bayesian Hierarchical User Modeling for Recommendation Systems, Proc. ACM SIGIR'07, pp. 47-54 (2007)
17. Liang, T.P., Lai, H.-J. and Ku, T.-C.: Personalized Content Recommendation and User Satisfaction: Theoretical Synthesis and Empirical Findings, Journal of Management Information Systems, Vol. 23, No. 3, pp. 45-70 (2007)
18. Hijikata, Y., Shimizu, T. and Nishida, S.: Discovery-oriented collaborative filtering for improving user satisfaction, Proc. ACM IUI'09, pp. 67-76 (2009)
19. Herlocker, J.L., Konstan, J.A. and Riedl, J.T.: Explaining Collaborative Filtering Recommendations, Proc. ACM CSCW'00, pp. 241-250 (2000)
20. Schafer, J.B, Konstan, J.A. and Riedl, J.: Meta-recommendation System: User-controlled Integration of Diverse Recommendation, Proc. ACM CIKM (2002)