Evaluating interface variants on personality acquisition for recommender systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5535 LNCS, Issue , 2009, Pages 259-270

  Dunn, Greg ^a   Wiersema, Jurgen ^b   Ham, Jaap ^c   Aroyo, Lora ^d  

^a PHILIPS RESEARCH LABORATORIES   (Netherlands)

^b CAPGEMINI   (Netherlands)

^c EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

^d VU UNIVERSITY AMSTERDAM   (Netherlands)

Author keywords

Adaptive systems; Cold start; Information acquisition; Personality; Recommender systems

Indexed keywords

ALTERNATIVE METHODS; COLD START; COLD START PROBLEMS; EASE OF USE; INFORMATION ACQUISITION; MEDIA CONTENT; PERSONALITY; PERSONALITY CHARACTERISTIC; POTENTIAL BENEFITS; RECOMMENDER SYSTEMS; USER CHARACTERISTICS; USER INFORMATION;

ADAPTIVE SYSTEMS; STARTING;

MERGERS AND ACQUISITIONS;

EID: 70349807517     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02247-0_25     Document Type: Conference Paper

References (24)

1. Costa, P.T., McCrae, R.R.: NEO PI-R Professional Manual. In: Psychological Assessment Resources, Odessa, FL (1992)
2. Deshpande, M., Karypis, G.: Item-Based Top-N Recommendation Algorithms. ACM Transactions on Information Systems 22, 143-177 (2004)
3. Gosling, S.: Snoop: What Your Stuff Says About You. Profile Books, London (2008)
4. Greenwald, A.G., Nosek, B.A., Banaji, M.R.: Understanding and Using the Implicit Association Test: I. An Improved Scoring Algorithm. J. of Pers. and Soc. Psych. 85, 197-216 (2003)
5. Grumm, M., von Collani, G.: Measuring Big-Five Personality Dimensions with the Implicit Association Test - Implicit Personality Traits or Self-Esteem? Pers. and Ind. Diff. 43, 2205-2217 (2007)
6. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22, 5-53 (2004)
7. Hoekstra, H.A., Ormel, J., de Fruyt, F.: NEO-PI-R/NEO-FFI Big Five Persoonlijkheidsvragenlijst: Handleiding. Harcourt Assessment, Lisse (2003)
8. Hofmann, T.: Latent Semantic Models for Collaborative Filtering. ACM Transactions on Information Systems 22, 89-115 (2004)
9. John, O.P., Srivastava, S.: The Big-Five Trait Taxonomy: History, Measurement, and Theoretical Perspectives. In: Pervin, L., John, O.P. (eds.) Handbook of Personality: Theory and Research, 2nd edn., pp. 102-138. Guilford, New York (1999)
10. Koole, S.L., Jager, W., van den Berg, A.E., Vlek, C.A.J., Hofstee, W.K.B.: On the Social Nature of Personality: Effects of Extraversion, Agreeableness, and Feedback about Collective Resource use on Cooperation in a Resource Dilemma. Pers. and Soc. Psych. Bulletin 27, 289-301 (2001)
11. Lam, X.N., Vu, T., Le, T.D., Duong, A.D.: Addressing Cold-Start Problem in Recommendation Systems. In: ICUIMC 2008, pp. 208-211. ACM Press, New York (2008)
12. Lekakos, G., Giaglis, G.M.: Improving the Prediction Accuracy of Recommendation Algorithms: Approaches Anchored on Human Factors. Int. with Comp. 18, 410-431 (2006)
13. McNee, S.M., Riedl, J., Konstan, J.A.: Making Recommendations Better: An Analytic Model for Human-Recommender Interaction. In: CHI 2006, pp. 1103-1108. ACM Press, New York (2006)
14. Nathanson, T., Bitton, E., Goldberg, K.: Eigentaste 5.0: Constant-Time Adaptability in a Recommender System Using Item Clustering. In: RecSys 2007, pp. 149-152. ACM Press, New York (2007)
15. Nguyen, A., Denos, N., Berrut, C.: Improving New User Recommendations with Rule- Based Induction on Cold User Data. In: RecSys 2007, pp. 121-128. ACM Press, New York (2007)
16. Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., McNee, S.M., Konstan, J.A., Riedl, J.: Getting to Know You: Learning New User Preferences in Recommender Systems. In: IUI 2002, pp. 127-134. ACM Press, New York (2002)
17. Rentfrow, P.J., Gosling, S.D.: The Do Re Mi's of Everyday Life: The Structure and Personality Correlates of Music Preferences. J. of Pers. and Soc. Psych. 84, 1236-1256 (2003)
18. Resnick, P., Varian, H.R.: Recommender Systems. Comm. of the ACM 40, 56-58 (1997)
19. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-Based Collaborative Filtering Recommendation Algorithms. In: WWW10, pp. 285-295. ACM Press, New York (2001)
20. Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and Metrics for Cold- Start Recommendations. In: SIGIR 2002, pp. 253-260. ACM Press, New York (2002)
21. Sinha, R., Swearingen, K.: The Role of Transparency in Recommender Systems. In: CHI 2002, pp. 830-831. ACM Press, New York (2002)
22. Steffens, M.C., Schulze König, S.: Predicting Spontaneous Big Five Behavior with Implicit Association Tests. European J. of Psychological Assessment 22, 13-20 (2006)
23. Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly 27, 425-478 (2003)
24. Zigoris, P., Zhang, Y.: Bayesian Adaptive User Profiling with Explicit and Implicit Feedback. In: CIKM 2006, pp. 397-404. ACM Press, New York (2006)