Abstract
Recommendation is a popular feature of social software. Recommendations could be made by the software autonomously or by social contacts who are often aided by the software on what to recommend. A great deal of emphasis in the literature has been given to the algorithmic solution to infer relevant and interesting recommendations. Yet, the delivery method of recommendation is still a widely unexplored research topic. This paper advocates that the success in deducing recommendations is not the sole factor for “recommendees” to consider. Users have their own requirements on the way a recommendation is made and delivered. Failure in meeting user expectations would often lead to the rejection of the recommendations as well as the violation of user experience. In this paper, we conduct an empirical research to explore such user’s perspective. We start with qualitative phase, based on interviews, and confirm and enhance the results in a quantitative phase through surveying a large sample of users. We report on the results and conclude with a set of guidelines on how recommendations delivery should be designed from a user’s perspective.
Chapter PDF
Similar content being viewed by others
References
Resnick, P., Varian, H.R.: Recommender systems. Communications of the ACM 40(3), 56–58 (1997)
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Communications of the ACM 35(12), 61–70 (1992)
Linden, G., Smith, B., York, J.: Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 7(1), 76–80 (2003)
Good, N., Schafer, J.B., Konstan, J.A., Borchers, A., Sarwar, B., Herlocker, J., Riedl, J.: Combining collaborative filtering with personal agents for better recommendations. In: Proceedings of AAAI/IAAI, pp. 439–446 (2003)
Cho, Y.H., Kim, J.K., Kim, S.H.: A personalized recommender system based on web usage mining and decision tree induction. Expert Systems with Applications 23(3), 329–342 (2002)
Pu, P., Chen, L., Hu, R.: Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Modeling and User-Adapted Interaction 22, 441–504 (2012)
McNee, S., Riedl, J., Konstan, J.: Making recommendations better: an analytic model for human-recommerder interaction. In: Proceedings of 24th International Conference Human Factors in Computing Systems (CHI), pp. 1103–1108 (2006)
Ozok, A.A., Fan, Q., Norcio, A.F.: Design guidelines for effective recommender system interfaces based on a usability criteria conceptual model: results from a college student population. International Journal of Behaviour Information Technology 29, 57–83 (2010)
Knijnenburg, B.P., Willemsen, M.C., Gantner, Z., Soncu, Newell, C.: Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction 22, 441–504 (2012)
McSherry, D.: Explanation in Recommender Systems. Artificial Intelligence Review 24, 179–197 (2005)
Tintarev, N., Judith, M.: A survey of explanations in recommender systems. In: IEEE 23rd International Conference on Data Engineering Workshop (2007)
Creswell, J.W., Clark, V.L.P.: Designing and conducting mixed methods research. SAGE Publications, Inc. (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 IFIP International Federation for Information Processing
About this paper
Cite this paper
Jiang, N., Ali, R. (2014). On the Delivery of Recommendations in Social Software: A User’s Perspective. In: Sauer, S., Bogdan, C., Forbrig, P., Bernhaupt, R., Winckler, M. (eds) Human-Centered Software Engineering. HCSE 2014. Lecture Notes in Computer Science, vol 8742. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44811-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-662-44811-3_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44810-6
Online ISBN: 978-3-662-44811-3
eBook Packages: Computer ScienceComputer Science (R0)