User-controllable personalization: A case study with SetFusion

International Journal of Human Computer Studies

Volumn 78, Issue , 2015, Pages 43-67

  Parra, Denis ^a   Brusilovsky, Peter ^b  

^a PONTIFICIA UNIVERSIDAD CATÓLICA DE CHILE   (Chile)

^b UNIVERSITY OF PITTSBURGH   (United States)

Author keywords

Hybrid recommender system; Interactive user interfaces; Recommender systems; User studies; User centric evaluation

Indexed keywords

RECOMMENDER SYSTEMS; USER INTERFACES;

BEHAVIORAL MEASURES; DESIGN AND IMPLEMENTATIONS; HYBRID RECOMMENDER SYSTEMS; INTERACTIVE USER INTERFACES; OBJECTIVE METRICS; USER PERCEPTIONS; USER STUDY; USER-CENTRIC EVALUATIONS;

INFORMATION FILTERING;

EID: 84924359867     PISSN: 10715819     EISSN: 10959300     Source Type: Journal    
DOI: 10.1016/j.ijhcs.2015.01.007     Document Type: Article

References (48)

1. D. Ariely Controlling the information flow: effects on consumers' decision making and preferences Journal of Consumer Research 27 2000 233 248
2. Albers, M.J., 1997. Cognitive strain as a factor in effective document design. In: Paper Presented at the Proceedings of the 15th Annual International Conference on Computer Documentation, Salt Lake City, Utah, USA.
3. B. Alsallakh, W. Aigner, S. Miksch, and H. Hauser Radial sets: interactive visual analysis of large overlapping sets IEEE Trans. Vis. Comput Graph. 19 12 2013 2496 2505
4. Attfield, S., Kazai, G., Lalmas, M., Piwowarski, B., 2011. Towards a Science of User Engagement WSDM Workshop on User Modeling for Web Applications.
5. R. Baeza-Yates, and B. Ribeiro-Neto Modern Information Retrieval vol. 463 1999 ACM Press New York
6. Bennett, J., Lanning, S., Netflix, N., 2007. The Netflix prize. In: Paper Presented at the In KDD Cup and Workshop in Conjunction with KDD.
7. Bostandjiev, S., O'Donovan, J., Höllerer, T., 2012. TasteWeights: a visual interactive hybrid recommender system. In: Paper Presented at the Proceedings of the Sixth ACM Conference on Recommender Systems, New York, NY, USA.
8. Bostandjiev, S., O'Donovan, J., Höllerer, T., 2013. LinkedVis: exploring social and semantic career recommendations. In: Paper Presented at the Proceedings of the 2013 International Conference on Intelligent User Interfaces, Santa Monica, California, USA.
9. J.E. Brown, and P.K. Dunn Comparisons of tobit, linear, and poisson-gamma regression models an application of time use data Sociol. Methods Res. 40 3 2011 511 535
10. Brusilovsky, P., Parra, D., Sahebi, S., Wongchokprasitti, C., 2010. Collaborative information finding in smaller communities: the case of research talks. In: Paper Presented at the CollaborateCom.
11. R. Burke Hybrid recommender systems: survey and experiments User Model. User-Adapted Interact. 12 4 2002 331 370
12. H. Cramer, V. Evers, S. Ramlal, M. van Someren, L. Rutledge, N. Stash, and B. Wielinga The effects of transparency on trust in and acceptance of a content-based art recommender User Model. User-Adapted Interact. 18 2008 455 496
13. M. Fink, J.-H. Haunert, A. Schulz, J. Spoerhase, and A. Wolff Algorithms for labeling focus regions IEEE Trans. Vis. Comput. Graph. 18 12 2012 2583 2592
14. D. Goldberg, D. Nichols, B.M. Oki, and D. Terry Using collaborative filtering to weave an information tapestry Commun. ACM 35 1992 61 70
15. Gretarsson, B., O'Donovan, J., Bostandjiev, S., Hall, C., Höllerer, T., 2010. Smallworlds: visualizing social recommendations. In: Paper Presented at the Proceedings of the 12th Eurographics/IEEE - VGTC Conference on Visualization, Bordeaux, France.
16. D.B. Hall Zero-inflated Poisson and binomial regression with random effects: a case study Biometrics 56 4 2000 1030 1039
17. Herlocker, J.L., Konstan, J.A., Riedl, J., 2000. Explaining collaborative filtering recommendations. In: Paper Presented at the Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work, New York, NY, USA.
18. J.L. Herlocker, J.A. Konstan, L.G. Terveen, and J.T. Riedl Evaluating collaborative filtering recommender systems ACM Trans. Inf. Syst. 22 2004 5 53
19. Hijikata, Y., Kai, Y., Nishida, S., 2012. The relation between user intervention and user satisfaction for information recommendation. In: Paper Presented at the Proceedings of the 27th Annual ACM Symposium on Applied Computing, New York, NY, USA.
20. Hu, Y., Koren, Y., Volinsky, C., 2008. Collaborative filtering for implicit feedback datasets. In: Paper Presented at the Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Washington, DC, USA.
21. Jameson, A., Schwarzkopf, E., 2006. Pros and cons of controllability: an empirical study. In: Paper Presented at the Adaptive Hypermedia and Adaptive Web-based Systems.
22. K. Järvelin, S. Price, L.L. Delcambre, and M. Nielsen Discounted cumulated gain based evaluation of multiple-query IR sessions C. Macdonald, I. Ounis, V. Plachouras, I. Ruthven, R. White, Advances in Information Retrieval vol. 4956 2008 Springer Berlin Heidelberg 4 15
23. Knijnenburg, B., Bostandjiev, S., O'Donovan, J., Kobsa, A., 2012. Inspectability and control in social recommenders. In: Paper Presented at the Proceedings of the Sixth ACM Conference on Recommender Systems, New York, NY, USA.
24. B. Knijnenburg, N. Rao, and A. Kobsa Experimental Materials used in the Study on Inspectability and Control in Social Recommender Systems 2012 Institute for Software Research, University of California, Irvine
25. Knijnenburg, B., Reijmer, N., Willemsen, M., 2011. Each to his own: how different users call for different interaction methods in recommender systems. In: Paper Presented at the Proceedings of the Fifth ACM Conference on Recommender Systems, New York, NY, USA.
26. B. Knijnenburg, M. Willemsen, Z. Gantner, H. Soncu, and C. Newell Explaining the user experience of recommender systems User Model. User-Adapted Interact. 22 4-5 2012 441 504
27. N.H. Lurie, and C.H. Mason Visual representation: implications for decision making J. Market. 71 2007 160 177
28. C.D. Manning, P. Raghavan, and H. Schütze Introduction to Information Retrieval 2008 Cambridge University Press New York, NY, USA
29. Marujo, L., Bugalho, M., Neto, J. P. d. S., Gershman, A., Carbonell, J., 2013. Hourly Traffic Prediction of News Stories. arXiv preprint, arXiv:1306.4608.
30. McNee, S.M., Riedl, J., Konstan, J.A., 2006a. Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: Paper Presented at the CHI '06 Extended Abstracts on Human Factors in Computing Systems, New York, NY, USA.
31. McNee, S.M., Riedl, J., Konstan, J.A., 2006b. Making recommendations better: an analytic model for human-recommender interaction. In: Paper Presented at the CHI '06 Extended Abstracts on Human Factors in Computing Systems, New York, NY, USA.
32. H.L. O'Brien, and E.G. Toms The development and evaluation of a survey to measure user engagement J. Am. Soc. Inf. Sci. Technol. 61 1 2010 50 69
33. O'Donovan, J., Smyth, B., Gretarsson, B., Bostandjiev, S., Höllerer, T., 2008. PeerChooser: visual interactive recommendation. In: Paper Presented at the Proceedings of the Twenty-Sixth Annual SIGCHI Conference on Human Factors in Computing Systems, New York, NY, USA.
34. Parra, D., Brusilovsky, P., 2013. A field study of a visual controllable talk recommender. In: Paper Presented at the Proceedings of the 2013 Chilean Conference on Human-Computer Interaction, Temuco, Chile.
35. Parra, D., Brusilovsky, P., Trattner, C., 2014. See what you want to see: visual user-driven approach for hybrid recommendation. In: Paper Presented at the Proceedings of the 19th International Conference on Intelligent User Interfaces, Haifa, Israel.
36. Parra, D., Jeng, W., Brusilovsky, P., López, C., Sahebi, S., 2012. Conference Navigator 3: an online social conference support system. In: Paper Presented at the Workshop and Poster Proceedings of the 20th Conference on User Modeling, Adaptation, and Personalization Montreal, Canada, July 16-20, 2012.
37. D. Parra, and S. Sahebi Recommender systems: sources of knowledge and evaluation metrics J.D.V.A. Squez, Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis 2013 Springer-Verlag Berlin Heidelberg 149 175
38. B. Prajapati, M. Dunne, and R. Armstrong Sample size estimation and statistical power analyses Optom. Today 16 2010 07
39. Pu, P., Chen, L., Hu, R., 2011. A user-centric evaluation framework for recommender systems. In: Paper Presented at the Proceedings of the Fifth ACM Conference on Recommender Systems, New York, NY, USA.
40. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J., 1994. GroupLens: an open architecture for collaborative filtering of netnews. In: Paper Presented at the Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, New York, NY, USA.
41. Shardanand, U., Maes, P., 1995. Social information filtering: algorithms for automating word of mouth. In: Paper Presented at the Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, New York, NY, USA.
42. E.H. Sherman, and E.H. Shortliffe A user-adaptable interface to predict users' needs M. Schneider-Hufschmidt, T. Kuhme, U. Malinowski, Adaptive User Interfaces: Principles and Practice 1993 North-Holland Amsterdam 285 316
43. Tintarev, N., Masthoff, J., 2007. Effective explanations of recommendations: user-centered design. In: Paper Presented at the Proceedings of the 2007 ACM Conference on Recommender Systems, New York, NY, USA.
44. N. Tintarev, and J. Masthoff Designing and evaluating explanations for recommender systems F. Ricci, L. Rokach, B. Shapira, P.B. Kantor, Recommender Systems Handbook 2011 Springer USA 479 510
45. Verbert, K., Parra, D., Brusilovsky, P., 2014. The effect of different set-based visualizations on user exploration of recommendations. In: Joint Workshop on Interfaces and Human Decision Making in Recommender Systems, p. 37.
46. Verbert, K., Parra, D., Brusilovsky, P., Duval, E., 2013. Visualizing recommendations to support exploration, transparency and controllability. In: Paper Presented at the Proceedings of the 2013 International Conference on Intelligent User Interfaces, Santa Monica, California, USA.
47. K.K. Yau, K. Wang, and A.H. Lee Zero-inflated negative binomial mixed regression modeling of over-dispersed count data with extra zeros Biom. J. 45 4 2003 437 452
48. Ziegler, C.-N., McNee, S.M., Konstan, J.A., Lausen, G., 2005. Improving recommendation lists through topic diversification. In: Paper Presented at the Proceedings of the 14th International Conference on World Wide Web, New York, NY, USA.