Abstract
Current research has shown the important role of explanation facilities in recommender systems based on the observation that explanations can significantly influence the user-perceived quality of such a system. In this paper we present and evaluate explanation interfaces in the form of tag clouds, which are a frequently used visualization and interaction technique on the Web. We report the result of a user study in which we compare the performance of two new explanation methods based on personalized and non-personalized tag clouds with a previous explanation approach. Overall, the results show that explanations based on tag clouds are not only well-accepted by the users but can also help to improve the efficiency and effectiveness of the explanation process. Furthermore, we provide first insights on the value of personalizing explanations based on the recently-proposed concept of item-specific tag preferences.
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References
Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: CSCW 2000, New York, pp. 241–250 (2000)
Tintarev, N., Masthoff, J.: Effective explanations of recommendations: User-centered design. In: RecSys 2007, New York, pp. 153–156 (2007)
Bilgic, M., Mooney, R.J.: Explaining recommendations: Satisfaction vs. promotion. In: Beyond Personalization 2005, San Diego, pp. 13–18 (2005)
Berry, D.C., Broadbent, D.E.: Explanation and verbalization in a computer-assisted search task. Quart. Journ. of Experim. Psychology 39(4), 585–609 (1987)
Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems - An Introduction. Cambridge University Press, Cambridge (2010)
Pu, P., Chen, L.: Trust building with expl. interfaces. In: IUI 2006, Sydney, pp. 93–100 (2006)
Vig, J., Sen, S., Riedl, J.: Tagsplanations: Explaining recommendations using tags. In: IUI 2009, Sanibel Island, Florida, pp. 47–56 (2009)
Cramer, H., Evers, V., Ramlal, S., Someren, M., Rutledge, L., Stash, N., Aroyo, L., Wielinga, B.: The effects of transparency on trust in and acceptance of a content-based art recommender. User Mod. and User-Adap. Inter. 18, 455–496 (2008)
Sen, S., Vig, J., Riedl, J.: Tagommenders: Connecting users to items through tags. In: WWW 2009, Madrid, pp. 671–680 (2009)
Kim, H.N., Ji, A.T., Ha, I., Jo, G.S.: Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation. Electronic Commerce Research and Applications 9(1), 73–83 (2010)
Gedikli, F., Jannach, D.: Rating items by rating tags. In: RSWEB 2010, Barcelona, pp. 25–32 (2010)
Vig, J., Soukup, M., Sen, S., Riedl, J.: Tag expression: Tagging with feeling. In: UIST 2010, New York, pp. 323–332 (2010)
Marlin, B.M., Zemel, R.S., Roweis, S., Slaney, M.: Collaborative filtering and the missing at random assumption. In: UAI 2007, Vancouver, pp. 267–275 (2007)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)
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Gedikli, F., Ge, M., Jannach, D. (2011). Understanding Recommendations by Reading the Clouds. In: Huemer, C., Setzer, T. (eds) E-Commerce and Web Technologies. EC-Web 2011. Lecture Notes in Business Information Processing, vol 85. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23014-1_17
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DOI: https://doi.org/10.1007/978-3-642-23014-1_17
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