Evaluating Recommender Systems with User Experiments



Proper evaluation of the user experience of recommender systems requires conducting user experiments. This chapter is a guideline for students and researchers aspiring to conduct user experiments with their recommender systems. It first covers the theory of user-centric evaluation of recommender systems, and gives an overview of recommender system aspects to evaluate. It then provides a detailed practical description of how to conduct user experiments, covering the following topics: formulating hypotheses, sampling participants, creating experimental manipulations, measuring subjective constructs with questionnaires, and statistically evaluating the results.


Confirmatory Factor Analysis Recommender System Preference Elicitation Average Variance Extract Situational Characteristic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. IEEE Transactions on Knowledge and Data Engineering 24(5), 896–911 (2012). DOI  10.1109/TKDE.2011.15 CrossRefGoogle Scholar
  2. 2.
    Ajzen, I.: From intentions to actions: A theory of planned behavior. In: P.D.J. Kuhl, D.J. Beckmann (eds.) Action Control, SSSP Springer Series in Social Psychology, pp. 11–39. Springer Berlin Heidelberg (1985).Google Scholar
  3. 3.
    Ajzen, I.: The theory of planned behavior. Organizational Behavior and Human Decision Processes 50(2), 179–211 (1991).CrossRefGoogle Scholar
  4. 4.
    Ajzen, I., Fishbein, M.: Understanding attitudes and predicting social behaviour. Prentice-Hall, Englewood Cliffs, NJ (1980)Google Scholar
  5. 5.
    Amatriain, X., Pujol, J.M., Tintarev, N., Oliver, N.: Rate it again: Increasing recommendation accuracy by user re-rating. In: Proceedings of the Third ACM Conference on Recommender Systems, RecSys ‘09, pp. 173–180. ACM, New York, NY, USA (2009). DOI  10.1145/1639714.1639744
  6. 6.
    Basartan, Y.: Amazon versus the shopbot: An experiment about how to improve the shopbots (2001)Google Scholar
  7. 7.
    Bennett, J., Lanning, S.: The netflix prize. In: In KDD Cup and Workshop in conjunction with KDD. San Jose, CA, USA (2007). URL
  8. 8.
    Bentler, P.M., Bonett, D.G.: Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin 88(3), 588–606 (1980). DOI  10.1037/0033-2909.88.3.588 CrossRefGoogle Scholar
  9. 9.
    Bettman, J.R., Luce, M.F., Payne, J.W.: Constructive consumer choice processes. Journal of consumer research 25(3), 187–217 (1998). DOI  10.1086/209535 CrossRefGoogle Scholar
  10. 10.
    Bilgic, M., Mooney, R.J.: Explaining recommendations: Satisfaction vs. promotion. In: IUI Workshop: Beyond Personalization. San Diego, CA (2005)Google Scholar
  11. 11.
    Blackwelder, W.C.: “Proving the null hypothesis” in clinical trials. Controlled Clinical Trials 3(4), 345–353 (1982). DOI  10.1016/0197-2456(82)90024-1 CrossRefGoogle Scholar
  12. 12.
    Bollen, D., Knijnenburg, B.P., Willemsen, M.C., Graus, M.: Understanding choice overload in recommender systems. In: Proceedings of the fourth ACM conference on Recommender systems, pp. 63–70. Barcelona, Spain (2010). DOI  10.1145/1864708.1864724
  13. 13.
    Bollen, K.A.: Structural equation models. In: Encyclopedia of Biostatistics. John Wiley & Sons, Ltd (2005)CrossRefGoogle Scholar
  14. 14.
    Bostandjiev, S., O’Donovan, J., Höllerer, T.: TasteWeights: a visual interactive hybrid recommender system. In: Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys ‘12, pp. 35–42. ACM, Dublin, Ireland (2012). DOI  10.1145/2365952.2365964
  15. 15.
    Cena, F., Vernero, F., Gena, C.: Towards a customization of rating scales in adaptive systems. In: P.D. Bra, A. Kobsa, D. Chin (eds.) User Modeling, Adaptation, and Personalization, no. 6075 in Lecture Notes in Computer Science, pp. 369–374. Springer Berlin Heidelberg (2010). DOI  10.1007/978-3-642-13470-8_34 Google Scholar
  16. 16.
    Chen, L., Pu, P.: Interaction design guidelines on critiquing-based recommender systems. User Modeling and User-Adapted Interaction 19(3), 167–206 (2009). DOI  10.1007/s11257-008-9057-x CrossRefGoogle Scholar
  17. 17.
    Chen, L., Pu, P.: Experiments on the preference-based organization interface in recommender systems. ACM Transactions on Computer-Human Interaction 17(1), 5:1–5:33 (2010). DOI  10.1145/1721831.1721836
  18. 18.
    Chen, L., Pu, P.: Eye-tracking study of user behavior in recommender interfaces. In: P.D. Bra, A. Kobsa, D. Chin (eds.) User Modeling, Adaptation, and Personalization, no. 6075 in Lecture Notes in Computer Science, pp. 375–380. Springer Berlin Heidelberg (2010). DOI  10.1007/978-3-642-13470-8_35 Google Scholar
  19. 19.
    Chen, L., Pu, P.: Critiquing-based recommenders: survey and emerging trends. User Modeling and User-Adapted Interaction 22(1–2), 125–150 (2012). DOI  10.1007/s11257-011-9108-6 CrossRefGoogle Scholar
  20. 20.
    Chen, L., Tsoi, H.K.: Users’ decision behavior in recommender interfaces: Impact of layout design. In: RecSys’ 11 Workshop on Human Decision Making in Recommender Systems, pp. 21–26. Chicago, IL, USA (2011). URL
  21. 21.
    Chin, D.N.: Empirical evaluation of user models and user-adapted systems. User Modeling and User-Adapted Interaction 11(1–2), 181–194 (2001). DOI  10.1023/A:1011127315884 CrossRefzbMATHGoogle Scholar
  22. 22.
    Cohen, J.: Statistical power analysis for the behavioral sciences. Psychology Press (1988)Google Scholar
  23. 23.
    Cosley, D., Lam, S.K., Albert, I., Konstan, J.A., Riedl, J.: Is seeing believing?: How recommender system interfaces affect users’ opinions. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ‘03, pp. 585–592. ACM, Ft. Lauderdale, Florida, USA (2003). DOI  10.1145/642611.642713
  24. 24.
    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 Modeling and User-Adapted Interaction 18(5), 455–496 (2008). DOI  10.1007/s11257-008-9051-3 CrossRefGoogle Scholar
  25. 25.
    Cremonesi, P., Garzotto, F., Negro, S., Papadopoulos, A.V., Turrin, R.: Looking for “Good” recommendations: A comparative evaluation of recommender systems. In: P. Campos, N. Graham, J. Jorge, N. Nunes, P. Palanque, M. Winckler (eds.) Human-Computer Interaction – INTERACT 2011, no. 6948 in Lecture Notes in Computer Science, pp. 152–168. Springer Berlin Heidelberg (2011). DOI  10.1007/978-3-642-23765-2_11 Google Scholar
  26. 26.
    Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13(3), 319–340 (1989). DOI  10.2307/249008 CrossRefGoogle Scholar
  27. 27.
    DeVellis, R.F.: Scale development: theory and applications. SAGE, Thousand Oaks, Calif. (2011)Google Scholar
  28. 28.
    Dooms, S., De Pessemier, T., Martens, L.: An online evaluation of explicit feedback mechanisms for recommender systems. In: 7th International Conference on Web Information Systems and Technologies (WEBIST-2011), pp. 391–394. Noordwijkerhout, The Netherlands (2011). URL
  29. 29.
    Dooms, S., De Pessemier, T., Martens, L.: A user-centric evaluation of recommender algorithms for an event recommendation system. In: RecSys 2011 Workshop on Human Decision Making in Recommender Systems (Decisions@ RecSys’ 11) and User-Centric Evaluation of Recommender Systems and Their Interfaces-2 (UCERSTI 2) affiliated with the 5th ACM Conference on Recommender Systems (RecSys 2011), pp. 67–73. Chicago, IL, USA (2011). URL
  30. 30.
    Downs, J.S., Holbrook, M.B., Sheng, S., Cranor, L.F.: Are your participants gaming the system?: screening mechanical turk workers. In: Proceedings of the 28th SIGCHI conference on Human factors in computing systems, pp. 2399–2402. Atlanta, Georgia, USA (2010). DOI  10.1145/1753326.1753688
  31. 31.
    Ekstrand, M.D., Harper, F.M., Willemsen, M.C., Konstan, J.A.: User perception of differences in recommender algorithms. In: Proceedings of the eighth ACM conference on Recommender systems. Foster City, CA (2014). DOI  10.1145/2645710.2645737 CrossRefGoogle Scholar
  32. 32.
    Erickson, B.H.: Some problems of inference from chain data. Sociological methodology 10(1), 276–302 (1979)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Farzan, R., Brusilovsky, P.: Encouraging user participation in a course recommender system: An impact on user behavior. Computers in Human Behavior 27(1), 276–284 (2011). DOI  10.1016/j.chb.2010.08.005 CrossRefGoogle Scholar
  34. 34.
    Fasolo, B., Hertwig, R., Huber, M., Ludwig, M.: Size, entropy, and density: What is the difference that makes the difference between small and large real-world assortments? Psychology and Marketing 26(3), 254–279 (2009). DOI  10.1002/mar.20272 CrossRefGoogle Scholar
  35. 35.
    Faul, F., Erdfelder, E., Lang, A.G., Buchner, A.: G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods 39(2), 175–191 (2007). DOI  10.3758/BF03193146 CrossRefGoogle Scholar
  36. 36.
    Felfernig, A.: Knowledge-based recommender technologies for marketing and sales. Intl. J. of Pattern Recognition and Artificial Intelligence 21(2), 333–354 (2007). DOI  10.1142/S0218001407005417 CrossRefGoogle Scholar
  37. 37.
    Fishbein, M., Ajzen, I.: Belief, attitude, intention, and behavior: an introduction to theory and research. Addison-Wesley Pub. Co., Reading, MA (1975)Google Scholar
  38. 38.
    Fisher, R.A.: The design of experiments, vol. xi. Oliver & Boyd, Oxford, England (1935)Google Scholar
  39. 39.
    Freyne, J., Jacovi, M., Guy, I., Geyer, W.: Increasing engagement through early recommender intervention. In: Proceedings of the Third ACM Conference on Recommender Systems, RecSys ‘09, pp. 85–92. ACM, New York, NY, USA (2009). DOI  10.1145/1639714.1639730
  40. 40.
    Friedrich, G., Zanker, M.: A taxonomy for generating explanations in recommender systems. AI Magazine 32(3), 90–98 (2011). DOI  10.1609/aimag.v32i3.2365
  41. 41.
    Gedikli, F., Jannach, D., Ge, M.: How should i explain? a comparison of different explanation types for recommender systems. International Journal of Human-Computer Studies 72(4), 367–382 (2014). DOI  10.1016/j.ijhcs.2013.12.007
  42. 42.
    Gena, C., Brogi, R., Cena, F., Vernero, F.: The impact of rating scales on user’s rating behavior. In: D. Hutchison, T. Kanade, J. Kittler, J.M. Kleinberg, F. Mattern, J.C. Mitchell, M. Naor, O. Nierstrasz, C. Pandu Rangan, B. Steffen, M. Sudan, D. Terzopoulos, D. Tygar, M.Y. Vardi, G. Weikum, J.A. Konstan, R. Conejo, J.L. Marzo, N. Oliver (eds.) User Modeling, Adaption and Personalization, vol. 6787, pp. 123–134. Springer, Berlin, Heidelberg (2011). DOI  10.1007/978-3-642-22362-4_11
  43. 43.
    Ghose, A., Ipeirotis, P.G., Li, B.: Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Science 31(3), 493–520 (2012). DOI  10.1287/mksc.1110.0700
  44. 44.
    Graus, M.P., Willemsen, M.C., Swelsen, K.: Understanding real-life website adaptations by investigating the relations between user behavior and user experience. In: F. Ricci, K. Bontcheva, O. Conlan, S. Lawless (eds.) User Modeling, Adaptation and Personalization, 9146, 350–356. Springer, Berlin, Heidelberg (2015)Google Scholar
  45. 45.
    Gregor, S.: The nature of theory in information systems. MIS Quarterly 30(3), 611–642 (2006). URL
  46. 46.
    Hassenzahl, M.: The thing and i: understanding the relationship between user and product. In: M. Blythe, K. Overbeeke, A. Monk, P. Wright (eds.) Funology, From Usability to Enjoyment, pp. 31–42. Kluwer Academic Publishers, Dordrecht, The Netherlands (2005). DOI  10.1007/1-4020-2967-5_4
  47. 47.
    Hassenzahl, M.: User experience (UX). In: Proceedings of the 20th International Conference of the Association Francophone d’Interaction Homme-Machine on - IHM ‘08, pp. 11–15. Metz, France (2008). DOI  10.1145/1512714.1512717
  48. 48.
    Häubl, G., Trifts, V.: Consumer decision making in online shopping environments: The effects of interactive decision aids. Marketing Science 19(1), 4–21 (2000). URL
  49. 49.
    Heckathorn, D.D.: Respondent-driven sampling II: deriving valid population estimates from chain-referral samples of hidden populations. Social problems 49(1), 11–34 (2002). DOI  10.1525/sp.2002.49.1.11
  50. 50.
    Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: Proc. of the 2000 ACM conference on Computer supported cooperative work, pp. 241–250. ACM Press, Philadelphia, PA (2000). DOI  10.1145/358916.358995
  51. 51.
    Hu, L., Bentler, P.M.: Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal 6(1), 1–55 (1999). DOI  10.1080/10705519909540118
  52. 52.
    Hu, R., Pu, P.: Enhancing recommendation diversity with organization interfaces. In: Proceedings of the 16th International Conference on Intelligent User Interfaces, IUI ‘11, pp. 347–350. ACM, Palo Alto, CA, USA (2011). DOI  10.1145/1943403.1943462
  53. 53.
    Iivari, J.: Contributions to the theoretical foundations of systemeering research and the PIOCO model. Ph.D. thesis, University of Oulu, Finland (1983)Google Scholar
  54. 54.
    Jacko, J.A.: The human-computer interaction handbook: fundamentals, evolving technologies, and emerging applications. CRC Press, Boca Raton, FL (2012)Google Scholar
  55. 55.
    Jackson, D.L.: Revisiting sample size and number of parameter estimates: Some support for the n:q hypothesis. Structural Equation Modeling: A Multidisciplinary Journal 10(1), 128–141 (2003). DOI  10.1207/S15328007SEM1001_6
  56. 56.
    Kahneman, D.: Thinking, fast and slow. Macmillan (2011)Google Scholar
  57. 57.
    Kammerer, Y., Gerjets, P.: How the interface design influences users’ spontaneous trustworthiness evaluations of web search results: Comparing a list and a grid interface. In: Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications, ETRA ‘10, pp. 299–306. ACM, Austin, TX, USA (2010). DOI  10.1145/1743666.1743736
  58. 58.
    Kittur, A., Chi, E.H., Suh, B.: Crowdsourcing user studies with mechanical turk. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 453–456. ACM Press, Florence, Italy (2008). DOI  10.1145/1357054.1357127
  59. 59.
    Kline, R.B.: Principles and practice of structural equation modeling. Guilford Press, New York (2011)Google Scholar
  60. 60.
    Kluver, D., Nguyen, T.T., Ekstrand, M., Sen, S., Riedl, J.: How many bits per rating? In: Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys ‘12, pp. 99–106. ACM, Dublin, Ireland (2012). DOI  10.1145/2365952.2365974
  61. 61.
    Knijnenburg, B.P.: Simplifying privacy decisions: Towards interactive and adaptive solutions. In: Proceedings of the Recsys 2013 Workshop on Human Decision Making in Recommender Systems (Decisions@ RecSys’13), pp. 40–41. Hong Kong, China (2013). URL
  62. 62.
    Knijnenburg, B.P., Bostandjiev, S., O’Donovan, J., Kobsa, A.: Inspectability and control in social recommenders. In: Proceedings of the sixth ACM conference on Recommender systems, RecSys ‘12, pp. 43–50. ACM, Dublin, Ireland (2012). DOI  10.1145/2365952.2365966
  63. 63.
    Knijnenburg, B.P., Kobsa, A.: Making decisions about privacy: Information disclosure in context-aware recommender systems. ACM Transactions on Interactive Intelligent Systems 3(3), 20:1–20:23 (2013). DOI  10.1145/2499670
  64. 64.
    Knijnenburg, B.P., Kobsa, A., Jin, H.: Dimensionality of information disclosure behavior. International Journal of Human-Computer Studies 71(12), 1144–1162 (2013). DOI  10.1016/j.ijhcs.2013.06.003
  65. 65.
    Knijnenburg, B.P., Reijmer, N.J., Willemsen, M.C.: Each to his own: how different users call for different interaction methods in recommender systems. In: Proceedings of the fifth ACM conference on Recommender systems, pp. 141–148. ACM Press, Chicago, IL, USA (2011). DOI  10.1145/2043932.2043960
  66. 66.
    Knijnenburg, B.P., Willemsen, M.C.: Understanding the effect of adaptive preference elicitation methods on user satisfaction of a recommender system. In: Proceedings of the third ACM conference on Recommender systems, pp. 381–384. New York, NY (2009). DOI  10.1145/1639714.1639793
  67. 67.
    Knijnenburg, B.P., Willemsen, M.C., Gantner, Z., Soncu, H., Newell, C.: Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction 22(4–5), 441–504 (2012). DOI  10.1007/s11257-011-9118-4
  68. 68.
    Knijnenburg, B.P., Willemsen, M.C., Hirtbach, S.: Receiving recommendations and providing feedback: The user-experience of a recommender system. In: F. Buccafurri, G. Semeraro (eds.) E-Commerce and Web Technologies, vol. 61, pp. 207–216. Springer, Berlin, Heidelberg (2010). DOI  10.1007/978-3-642-15208-5_19
  69. 69.
    Knijnenburg, B.P., Willemsen, M.C., Kobsa, A.: A pragmatic procedure to support the user-centric evaluation of recommender systems. In: Proceedings of the fifth ACM conference on Recommender systems, RecSys ‘11, pp. 321–324. ACM, Chicago, IL, USA (2011). DOI  10.1145/2043932.2043993
  70. 70.
    Kobsa, A., Cho, H., Knijnenburg, B.P.: An attitudinal and behavioral model of personalization at different providers. Journal of the Association for Information Science and Technology. (In press)
  71. 71.
    Köhler, C.F., Breugelmans, E., Dellaert, B.G.C.: Consumer acceptance of recommendations by interactive decision aids: The joint role of temporal distance and concrete versus abstract communications. Journal of Management Information Systems 27(4), 231–260 (2011). DOI  10.2753/MIS0742-1222270408
  72. 72.
    Konstan, J., Riedl, J.: Recommender systems: from algorithms to user experience. User Modeling and User-Adapted Interaction 22(1), 101–123 (2012). DOI  10.1007/s11257-011-9112-x
  73. 73.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009). DOI  10.1109/MC.2009.263
  74. 74.
    Koren, Y., Sill, J.: OrdRec: An ordinal model for predicting personalized item rating distributions. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys ‘11, pp. 117–124. ACM, New York, NY, USA (2011). DOI  10.1145/2043932.2043956
  75. 75.
    Landsberger, H.A.: Hawthorne revisited: Management and the worker: its critics, and developments in human relations in industry. Cornell University (1958)Google Scholar
  76. 76.
    Lathia, N., Hailes, S., Capra, L., Amatriain, X.: Temporal diversity in recommender systems. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ‘10, pp. 210–217. ACM, Geneva, Switzerland (2010). DOI  10.1145/1835449.1835486
  77. 77.
    Lee, Y.E., Benbasat, I.: The influence of trade-off difficulty caused by preference elicitation methods on user acceptance of recommendation agents across loss and gain conditions. Information Systems Research 22(4), 867–884 (2011). DOI  10.1287/isre.1100.0334
  78. 78.
    Lopes, C.S., Rodrigues, L.C., Sichieri, R.: The lack of selection bias in a snowball sampled case-control study on drug abuse. International journal of epidemiology 25(6), 1267–1270 (1996). DOI  10.1093/ije/25.6.1267
  79. 79.
    MacCallum, R.C., Widaman, K.F., Zhang, S., Hong, S.: Sample size in factor analysis. Psychological Methods 4(1), 84–99 (1999). DOI  10.1037/1082-989X.4.1.84
  80. 80.
    MacKenzie, I.S.: Human-Computer Interaction: An Empirical Research Perspective, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2013)Google Scholar
  81. 81.
    Martin, F.J.: Recsys’09 industrial keynote: Top 10 lessons learned developing deploying and operating real-world recommender systems. In: Proceedings of the Third ACM Conference on Recommender Systems, RecSys ‘09, pp. 1–2. ACM, New York, NY, USA (2009). DOI  10.1145/1639714.1639715
  82. 82.
    McNee, S.M., Albert, I., Cosley, D., Gopalkrishnan, P., Lam, S.K., Rashid, A.M., Konstan, J.A., Riedl, J.: On the recommending of citations for research papers. In: Proceedings of the 2002 ACM conference on Computer supported cooperative work, pp. 116–125. New Orleans, LA (2002). DOI  10.1145/587078.587096
  83. 83.
    McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: Extended abstracts on Human factors in computing systems, pp. 1097–1101. Montréal, Québec, Canada (2006). DOI  10.1145/1125451.1125659
  84. 84.
    McNee, S.M., Riedl, J., Konstan, J.A.: Making recommendations better: An analytic model for human-recommender interaction. In: Extended Abstracts on Human Factors in Computing Systems, CHI EA ‘06, pp. 1103–1108. ACM, Montréal, Québec, Canada (2006). DOI  10.1145/1125451.1125660
  85. 85.
    Mogilner, C., Rudnick, T., Iyengar, S.S.: The mere categorization effect: How the presence of categories increases choosers’ perceptions of assortment variety and outcome satisfaction. Journal of Consumer Research 35(2), 202–215 (2008). DOI  10.1086/586908
  86. 86.
    Neter, J., Kutner, M.H., Nachtsheim, C.J., Wasserman, W.: Applied linear statistical models, vol. 4. Irwin Chicago (1996)Google Scholar
  87. 87.
    Nguyen, T.T., Kluver, D., Wang, T.Y., Hui, P.M., Ekstrand, M.D., Willemsen, M.C., Riedl, J.: Rating support interfaces to improve user experience and recommender accuracy. In: Proceedings of the 7th ACM Conference on Recommender Systems, RecSys ‘13, pp. 149–156. ACM, Hong Kong, China (2013). DOI  10.1145/2507157.2507188
  88. 88.
    Nuzzo, R.: Scientific method: Statistical errors. Nature 506(7487), 150–152 (2014). DOI  10.1038/506150a
  89. 89.
    Oestreicher-Singer, G., Sundararajan, A.: Recommendation networks and the long tail of electronic commerce. Management Information Systems Quarterly 36(1), 65–83 (2012). URL
  90. 90.
    Oestreicher-Singer, G., Sundararajan, A.: The visible hand? demand effects of recommendation networks in electronic markets. Management Science 58(11), 1963–1981 (2012). DOI  10.1287/mnsc.1120.1536
  91. 91.
    Orne, M.T.: On the social psychology of the psychological experiment: With particular reference to demand characteristics and their implications. American Psychologist 17(11), 776–783 (1962). DOI  10.1037/h0043424
  92. 92.
    Paolacci, G., Chandler, J., Ipeirotis, P.: Running experiments on amazon mechanical turk. Judgment and Decision Making 5(5), 411–419 (2010). URL
  93. 93.
    Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y., Podsakoff, N.P.: Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology 88(5), 879–903 (2003). DOI  10.1037/0021-9010.88.5.879
  94. 94.
    Pu, P., Chen, L.: Trust-inspiring explanation interfaces for recommender systems. Knowledge-Based Systems 20(6), 542–556 (2007). DOI  10.1016/j.knosys.2007.04.004
  95. 95.
    Pu, P., Chen, L., Hu, R.: A user-centric evaluation framework for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys ‘11, pp. 157–164. ACM, Chicago, IL, USA (2011). DOI  10.1145/2043932.2043962
  96. 96.
    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(4), 317–355 (2012). DOI  10.1007/s11257-011-9115-7
  97. 97.
    Purchase, H.C.: Experimental Human-Computer Interaction: A Practical Guide with Visual Examples, 1st edn. Cambridge University Press, New York, NY, USA (2012)Google Scholar
  98. 98.
    Randall, T., Terwiesch, C., Ulrich, K.T.: User design of customized products. Marketing Science 26(2), 268–280 (2007). DOI  10.1287/mksc.1050.0116
  99. 99.
    Said, A., Fields, B., Jain, B.J., Albayrak, S.: User-centric evaluation of a k-furthest neighbor collaborative filtering recommender algorithm. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work, CSCW ‘13, pp. 1399–1408. ACM, New York, NY, USA (2013). DOI  10.1145/2441776.2441933
  100. 100.
    Said, A., Jain, B.J., Narr, S., Plumbaum, T., Albayrak, S., Scheel, C.: Estimating the magic barrier of recommender systems: A user study. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ‘12, pp. 1061–1062. ACM, Portland, Oregon (2012). DOI  10.1145/2348283.2348469
  101. 101.
    Salganik, M.J., Heckathorn, D.D.: Sampling and estimation in hidden populations using respondent-driven sampling. Sociological Methodology 34(1), 193–240 (2004). DOI  10.1111/j.0081-1750.2004.00152.x
  102. 102.
    Schaeffer, N.C., Presser, S.: The science of asking questions. Annual Review of Sociology 29(1), 65–88 (2003). DOI  10.1146/annurev.soc.29.110702.110112
  103. 103.
    Scheibehenne, B., Greifeneder, R., Todd, P.M.: Can there ever be too many options? a Meta-Analytic review of choice overload. Journal of Consumer Research 37(3), 409–425 (2010). DOI  10.1086/651235
  104. 104.
    Sinha, R., Swearingen, K.: Comparing recommendations made by online systems and friends. In: In Proceedings of the DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries (2001)Google Scholar
  105. 105.
    Smith, N.C., Goldstein, D.G., Johnson, E.J.: Choice without awareness: Ethical and policy implications of defaults. Journal of Public Policy & Marketing 32(2), 159–172 (2013). DOI  10.1509/jppm.10.114
  106. 106.
    Sparling, E.I., Sen, S.: Rating: How difficult is it? In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys ‘11, pp. 149–156. ACM, Chicago, IL, USA (2011). DOI  10.1145/2043932.2043961
  107. 107.
    Steele-Johnson, D., Beauregard, R.S., Hoover, P.B., Schmidt, A.M.: Goal orientation and task demand effects on motivation, affect, and performance. Journal of Applied Psychology 85(5), 724–738 (2000). DOI  10.1037/0021-9010.85.5.724
  108. 108.
    Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Providing justifications in recommender systems. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 38(6), 1262–1272 (2008). DOI  10.1109/TSMCA.2008.2003969
  109. 109.
    Tam, K.Y., Ho, S.Y.: Web personalization: is it effective? IT Professional 5(5), 53–57 (2003). DOI  10.1109/MITP.2003.1235611
  110. 110.
    Tintarev, N., Masthoff, J.: A survey of explanations in recommender systems. In: Data Engineering Workshop, pp. 801–810. IEEE, Istanbul, Turkey (2007). DOI  10.1109/ICDEW.2007.4401070
  111. 111.
    Tintarev, N., Masthoff, J.: Evaluating the effectiveness of explanations for recommender systems. User Modeling and User-Adapted Interaction 22(4–5), 399–439 (2012). DOI  10.1007/s11257-011-9117-5
  112. 112.
    Torres, R., McNee, S.M., Abel, M., Konstan, J.A., Riedl, J.: Enhancing digital libraries with TechLens+. In: Proceedings of the 2004 joint ACM/IEEE conference on Digital libraries - JCDL ‘04, pp. 228–236. Tuscon, AZ, USA (2004). DOI  10.1145/996350.996402
  113. 113.
    Utts, J.: Seeing Through Statistics. Cengage Learning (2004)Google Scholar
  114. 114.
    Van Velsen, L., Van Der Geest, T., Klaassen, R., Steehouder, M.: User-centered evaluation of adaptive and adaptable systems: a literature review. The Knowledge Engineering Review 23(03), 261–281 (2008). DOI  10.1017/S0269888908001379
  115. 115.
    Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys ‘11, pp. 109–116. ACM, Chicago, IL, USA (2011). DOI  10.1145/2043932.2043955
  116. 116.
    Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: Toward a unified view. MIS Quarterly 27(3), 425–478 (2003). URL
  117. 117.
    Vig, J., Sen, S., Riedl, J.: Tagsplanations: Explaining recommendations using tags. In: Proceedings of the 14th International Conference on Intelligent User Interfaces, IUI ‘09, pp. 47–56. ACM, Sanibel Island, Florida, USA (2009). DOI  10.1145/1502650.1502661
  118. 118.
    Wang, H.C., Doong, H.S.: Argument form and spokesperson type: The recommendation strategy of virtual salespersons. International Journal of Information Management 30(6), 493–501 (2010). DOI  10.1016/j.ijinfomgt.2010.03.006
  119. 119.
    Wang, W., Benbasat, I.: Recommendation agents for electronic commerce: Effects of explanation facilities on trusting beliefs. Journal of Management Information Systems 23(4), 217–246 (2007). DOI  10.2753/MIS0742-1222230410
  120. 120.
    Willemsen, M.C., Graus, M.P., Knijnenburg, B.P.: Understanding the role of latent feature diversification on choice difficulty and satisfaction (manuscript, under review)Google Scholar
  121. 121.
    Willemsen, M.C., Knijnenburg, B.P., Graus, M.P., Velter-Bremmers, L.C., Fu, K.: Using latent features diversification to reduce choice difficulty in recommendation lists. In: RecSys’11 Workshop on Human Decision Making in Recommender Systems, CEUR-WS, vol. 811, pp. 14–20. Chicago, IL (2011). URL
  122. 122.
    Xiao, B., Benbasat, I.: E-commerce product recommendation agents: Use, characteristics, and impact. Mis Quarterly 31(1), 137–209 (2007). URL
  123. 123.
    Xiao, B., Benbasat, I.: Research on the use, characteristics, and impact of e-commerce product recommendation agents: A review and update for 2007–2012. In: F.J. Martínez-López (ed.) Handbook of Strategic e-Business Management, Progress in IS, pp. 403–431. Springer Berlin Heidelberg (2014). DOI  10.1007/978-3-642-39747-9_18
  124. 124.
    Zhang, M., Hurley, N.: Avoiding monotony: Improving the diversity of recommendation lists. In: Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys ‘08, pp. 123–130. ACM, Lausanne, Switzerland (2008). DOI  10.1145/1454008.1454030
  125. 125.
    Zhou, T., Kuscsik, Z., Liu, J.G., Medo, M., Wakeling, J.R., Zhang, Y.C.: Solving the apparent diversity-accuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences 107(10), 4511–4515 (2010). DOI  10.1073/pnas.1000488107
  126. 126.
    Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th international conference on World Wide Web - WWW ‘05, pp. 22–32. Chiba, Japan (2005). DOI  10.1145/1060745.1060754

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Clemson UniversityClemsonUSA
  2. 2.Eindhoven University of TechnologyEindhovenThe Netherlands

Personalised recommendations