Abstract
This paper uses a User-as-Wizard approach to evaluate how people apply diversity to a set of recommendations. In particular, it considers how diversity is applied for a recipient with high or low Openness to Experience, a personality trait from the Five Factor Model. While there was no effect of the personality trait on the degree of diversity applied, there seems to be a trend in the way in which it was applied. Maximal categorical diversity (across genres) was more likely to be applied to those with high Openness to Experience, at the expense of maximal thematic diversity (within genres).
Keywords
- Diversity
- Serendipity
- Personality
- Recommender Systems
This research has been funded by the Engineering and Physical Sciences Research Council (EPSRC, UK), grant ref. EP/J012084/1.
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Abbassi, Z., Mirrokni, V.S., Thakur, M.: Diversity maximization under matroid constraints. Technical report, Department of Computer Science, Columbia University (2012)
Bridge, D., Kelly, J.P.: Ways of computing diverse collaborative recommendations. In: Wade, V.P., Ashman, H., Smyth, B. (eds.) AH 2006. LNCS, vol. 4018, pp. 41–50. Springer, Heidelberg (2006)
Smyth, B., McClave, P.: Similarity vs. Diversity. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 347–361. Springer, Heidelberg (2001)
Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: WWW 2005, pp. 22–32 (2005)
Workshop on Novelty and Diversity in Recommender Systems, DiveRS 2011 (2011)
Goldberg, L.: The structure of phenotypic personality traits. American Psychologist 48, 26–34 (1993)
Nunes, M.A.S.N.: Recommender Systems based on Personality Traits. PhD thesis, Universite Montpellier 2 (2008)
Costa, P.T., McCrae, R.R.: NEO personality Inventory professional manual. Psychological Assessment Resources, Odessa (1992)
Herlocker, J.L., Konstan, J.A., Terveen, L., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)
Said, A., Fields, B., Jain, B.J., Albayrak, S.: User-centric evaluation of a k-furthest neighbor collaborative filtering recommender algorithm. In: CSCW (2013)
APA: Diagnostic and Statistical Manual of Mental Disorders. 4th edn. American Psychiatric Association (2000)
Dunn, G., Wiersema, J., Ham, J., Aroyo, L.: Evaluating interface variants on personality acquisition for recommender systems. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 259–270. Springer, Heidelberg (2009)
Lin, C.H., Mcleod, D.: Exploiting and learning human temperaments for customized information recommendation. In: IMSA (2002)
Hu, R., Pu, P.: Enhancing collaborative filtering systems with personality information. In: Recsys (2011)
Hu, R., Pu, P.: Acceptance issues of personality-based recommender systems. In: Recsys (2009)
Pu, P., Chen, L., Hu, R.: Evaluating recommender systems from the users perspective: survey of the state of the art. UMUAI 22, 317–355 (2012)
Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. IEEE Transactions on Knowledge and Data Engineering 24, 896–911 (2011)
Golbeck, J., Hansen, D.L.: A framework for recommending collections. In: Workshop on Novelty and Diversity in Recommender Systems in Conjuction with Recsys (2011)
Pu, P., Chen, L., Hu, R.: A user-centric evaluation framework for recommender systems. In: Recsys (2011)
Adamopoulos, P., Tuzhilin, A.: On unexpectedness in recommender systems: Or how to except the unexpected. In: Workshop on Novelty and Diversity in Recommender Systems in Conjuction with Recsys (2011)
MT: Amazon mechanical turk, http://www.mturk.com
Sinha, R., Swearingen, K.: Comparing recommendations made by online systems and friends. In: Proceedings of the DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries (2001)
Masthoff, J.: The user as wizard: A method for early involvement in the design and evaluation of adaptive systems. In: Fifth Workshop on User-Centred Design and Evaluation of Adaptive Systems, vol. 1, pp. 460–469 (2006)
Paramythis, A., Weibelzahl, S., Masthoff, J.: Layered evaluation of interactive adaptive systems: framework and formative methods. UMUAI 20, 383–453 (2010)
Taylor, W.L.: Cloze procedure: A new tool for measuring readability. Journalism Quarterly 30, 415–433 (1953)
Gosling, S.D., Rentfrow, P.J., Swann Jr., W.B.: A very brief measure of the big five personality domains. Journal of Research in Personality 37, 504–528 (2003)
Goz-lab: Tipi normal values, http://tiny.cc/9otwqw
Dennis, M., Masthoff, J., Mellish, C.: The quest for validated personality trait stories. In: IUI (2012)
Goldberg, L.R., Johnson, J.A., Eber, H.W., Hogan, R., Ashton, M.C., Cloninger, C.R., Gough, H.G.: The international personality item pool and the future of public-domain personality measures. Journal of Research in Personality 40(1), 84–96 (2006)
Tintarev, N., Masthoff, J.: Over- and underestimation in different product domains. In: Workshop on Recommender Systems in Conjunction with the European Conference on Artificial Intelligence, pp. 14–19 (2008)
Tintarev, N., Masthoff, J.: Designing and evaluating explanations for recommender systems. In: Kantor, P.B., Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, Springer (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tintarev, N., Dennis, M., Masthoff, J. (2013). Adapting Recommendation Diversity to Openness to Experience: A Study of Human Behaviour. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds) User Modeling, Adaptation, and Personalization. UMAP 2013. Lecture Notes in Computer Science, vol 7899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38844-6_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-38844-6_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38843-9
Online ISBN: 978-3-642-38844-6
eBook Packages: Computer ScienceComputer Science (R0)