Skip to main content
SpringerLink
Log in

How Contextual Data Influences User Experience with Scholarly Recommender Systems: An Empirical Framework

  • Conference paper
  • First Online:
HCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12423))

Included in the following conference series:

Abstract

Since the advent of Recommender Systems (RSs), many papers have been published with the majority focusing on creating more accurate algorithms. The more accurate the algorithm is, the better the recommendation is predicted to be for users. Recently, RSs researchers pointed out that the embedding of the recommendation methods in the User Experience (UX) dramatically affects the recommender systems’ value to users. This paper proposes a framework to explore how contexts influence UX with Scholarly recommender systems and identifies relevant contexts to be incorporated in the UX. We first review existing models and theories of UX that are most applicable to RSs and identify gaps in existing work. The framework clarifies how contexts can influence UX with SRSs and enriches our conceptual understanding of how contextual information influences UX of scholarly recommender systems. It can serve as a foundation for further theoretical and empirical investigation. An experiment evaluating the user experience is performed using the quantitative method of Partial Least Squares (PLS) Regression and Structural Equation Modeling (SEM) to examine the developed conceptual framework.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Beel, J., et al.: Research paper recommender system evaluation: a quantitative literature survey. In: Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation. ACM (2013)

    Google Scholar 

  2. Beel, J., et al.: Paper recommender systems: a literature survey. Int. J. Digit. Libraries 17(4), 305–338 (2016)

    Article  Google Scholar 

  3. McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: CHI 2006 Extended Abstracts on Human Factors in Computing Systems. ACM (2006)

    Google Scholar 

  4. Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User-Adapt. Interact. 22(1), 101–123 (2012)

    Article  Google Scholar 

  5. Nguyen, T.: Enhancing user experience with recommender systems beyond prediction accuracies. Ph.D Dissertation, The University of Minnesota (2016)

    Google Scholar 

  6. Knijnenburg, B.P., et al.: Explaining the user experience of recommender systems. User Model. User-Adapt. Interact. 22(4–5), 441–504 (2012)

    Article  Google Scholar 

  7. Champiri, Z.D., et al.: User experience and recommender systems. In: 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). IEEE (2019)

    Google Scholar 

  8. Hassenzahl, M., Tractinsky, N.: User experience-a research agenda. Behav. Inf. Technol. 25(2), 91–97 (2006)

    Article  Google Scholar 

  9. McCarthy, J., Wright, P.: Technology as experience. Interactions 11(5), 42–43 (2004)

    Article  Google Scholar 

  10. Law, E.L.-C., et al.: Understanding, scoping and defining user experience: a survey approach. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM (2009)

    Google Scholar 

  11. Champiri, Z.D., Shahamiri, S.R., Salim, S.S.B.: A systematic review of scholar context-aware recommender systems. Expert Syst. Appl. 42(3), 1743–1758 (2015)

    Article  Google Scholar 

  12. Kraft, C.: User Experience Innovation: User Centered Design that Works. Apress, New York (2012)

    Book  Google Scholar 

  13. Bernhaupt, R.: Evaluating User Experience in Games: Concepts and Methods, p. 277. Springer, London (2010). https://doi.org/10.1007/978-1-84882-963-3

    Book  Google Scholar 

  14. Dehghani Champiri, Z., Asemi, A., Siti Salwah Binti, S.: Meta-analysis of evaluation methods and metrics used in context-aware scholarly recommender systems. Knowl. Inf. Syst. 61(2), 1147–1178 (2019). https://doi.org/10.1007/s10115-018-1324-5

    Article  Google Scholar 

  15. Champiri, Z., Salim, S.S., Shahamiri, S.R.: The role of context for recommendations in digital libraries. Int. J. Soc. Sci. Humanity 5(11), 948–954 (2015)

    Article  Google Scholar 

  16. Hart, J.: Investigating user experience and user engagement for design (2015)

    Google Scholar 

  17. Visser, F.S., et al.: Contextmapping: experiences from practice. CoDesign 1(2), 119–149 (2005)

    Article  Google Scholar 

  18. Monk, A., et al.: Funology: designing enjoyment. In: CHI 2002 Extended Abstracts on Human Factors in Computing Systems. ACM (2002)

    Google Scholar 

  19. Green, W.S., Jordan, P.W.: Pleasure with Products: Beyond Usability. CRC Press, Boca Raton (2003)

    Google Scholar 

  20. Tractinsky, N., Katz, A.S., Ikar, D.: What is beautiful is usable. Interact. Comput. 13(2), 127–145 (2000)

    Article  Google Scholar 

  21. Sanders, E.B.-N., From user-centered to participatory design approaches. In: Design and the Social Sciences: Making Connections, vol. 1, no. 8 (2002)

    Google Scholar 

  22. Norman, D.: The Design of Everyday Things, Revised and expanded edition edn. Basic Books, New York (2013)

    Google Scholar 

  23. Pu, P., Chen, L., Hu, R.: A user-centric evaluation framework for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems. ACM (2011)

    Google Scholar 

  24. Mcnee, S.M.: Meeting user information needs in recommender systems. Proquest (2006)

    Google Scholar 

  25. McNee, S.M., Riedl, J., Konstan, J.A.: Making recommendations better: an analytic model for human-recommender interaction. In: CHI 2006 Extended Abstracts on Human Factors in Computing Systems. ACM (2006)

    Google Scholar 

  26. Fishbein, M., Ajzen, L.: Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, pp. 181–202. Addison-Wesley, Reading (1975)

    Google Scholar 

  27. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35(8), 982–1003 (1989)

    Article  Google Scholar 

  28. Venkatesh, V., et al.: User acceptance of information technology: toward a unified view. MIS Q. 27, 425–478 (2003)

    Article  Google Scholar 

  29. Hartmann, J., Sutcliffe, A., Angeli, A.D.: Towards a theory of user judgment of aesthetics and user interface quality. ACM Trans. Comput.-Hum. Interact. (TOCHI) 15(4), 15 (2008)

    Article  Google Scholar 

  30. 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. Behav. Inf. Technol. 29(1), 57–83 (2010)

    Article  Google Scholar 

  31. Bauernfeind, U., Zins, A.H.: The perception of exploratory browsing and trust with recommender websites. Inf. Technol. Tour. 8(2), 121–136 (2005)

    Article  Google Scholar 

  32. Xiao, B., Benbasat, I.: E-commerce product recommendation agents: use, characteristics, and impact. MIS Q. 31(1), 137–209 (2007)

    Article  Google Scholar 

  33. Thüring, M., Mahlke, S.: Usability, aesthetics and emotions in human–technology interaction. Int. J. Psychol. 42(4), 253–264 (2007)

    Article  Google Scholar 

  34. Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_7

    Chapter  Google Scholar 

  35. Panniello, U., Gorgoglione, M.: Context-aware recommender systems: a comparison of three approaches. In: DART@ AI* IA (2011)

    Google Scholar 

  36. Baltrunas, L., et al.: Context relevance assessment and exploitation in mobile recommender systems. Pers. Ubiquitous Comput. 16(5), 507–526 (2012)

    Article  Google Scholar 

  37. Hassenzahl, M., et al.: Designing moments of meaning and pleasure. Exp. Des. Happiness Int. J. Des. 7(3), 21–31 (2013)

    Google Scholar 

  38. Felfernig, A., Burke, R., Pu, P.: Preface to the special issue on user interfaces for recommender systems. User Model. User-Adapted Interact. 22(4), 313–316 (2012)

    Article  Google Scholar 

  39. Kuo, J.-J., Zhang, Y.-J.: A library recommender system using interest change over time and matrix clustering. In: Chen, H.-H., Chowdhury, G. (eds.) ICADL 2012. LNCS, vol. 7634, pp. 259–268. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34752-8_32

    Chapter  Google Scholar 

  40. McNee, S.M., Kapoor, N., Konstan, J.A.: Don’t look stupid: avoiding pitfalls when recommending research papers. In: Proceedings of the 2006 20th Anniversary Conference on Computer Supported Cooperative Work. ACM (2006)

    Google Scholar 

  41. Marko A., Rodriguez, D.W.A., Shinavier, J., Ebersole, G.: A recommender system to support the scholarly communication process. CoRR abs/0905.1594 (2009)

    Google Scholar 

  42. Konstan, J.A., et al.: Techlens: Exploring the use of recommenders to support users of digital libraries. In: CNI Fall Task Force Meeting Project Briefing. Coalition for Networked Information, Phoenix, AZ (2005)

    Google Scholar 

  43. Dehghani, Z., et al.: A multi-layer contextual model for recommender systems in digital libraries. In: Aslib Proceedings, vol. 63, no. 6, pp. 555–569 (2011)

    Google Scholar 

  44. Patton, R., Potok, T., Worley, B.: Discovery & refinement of scientific information via a recommender system. In: The Second International Conference on Advanced Communications and Computation INFOCOMP 2012 (2012)

    Google Scholar 

  45. Rodriguez, M.A., et al.: A recommender system to support the scholarly communication process. arXiv preprint arXiv:0905.1594 (2009)

  46. Rana, C.: New dimensions of temporal serendipity and temporal novelty in recommender system. Adv. Appl. Sci. Res. 4(1), 151–157 (2013)

    Google Scholar 

  47. Adamopoulos, P., Tuzhilin, A.: On unexpectedness in recommender systems: or how to better expect the unexpected. ACM Trans. Intell. Syst. Technol. (TIST) 5(4), 54 (2015)

    Google Scholar 

  48. Strangman, N., Hall, T.: Background knowledge. National Center on Assessing the General Curriculum, Wakefield, MA, vol. 26, p. 2005 (2004). Accessed Sept 2004

    Google Scholar 

  49. Amini, B., et al.: Incorporating scholar’s background knowledge into recommender system for digital libraries. In: 2011 5th Malaysian Conference in Software Engineering (MySEC). IEEE (2011)

    Google Scholar 

  50. McNee, S.M., et al.: On the recommending of citations for research papers. In: Proceedings of the 2002 ACM Conference on Computer Supported Cooperative Work. ACM (2002)

    Google Scholar 

  51. Jannach, D., Lerche, L., Gedikli, F., Bonnin, G.: What recommenders recommend – an analysis of accuracy, popularity, and sales diversity effects. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds.) UMAP 2013. LNCS, vol. 7899, pp. 25–37. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38844-6_3

    Chapter  Google Scholar 

  52. Serrano-Guerrero, J., et al.: A Google wave-based fuzzy recommender system to disseminate information in University Digital Libraries 2.0. Inf. Sci. 181(9), 1503–1516 (2011)

    Article  Google Scholar 

  53. Yang, W.-S., Lin, Y.-R.: A task-focused literature recommender system for digital libraries. Online Inf. Rev. 37(4), 581–601 (2013)

    Article  MathSciNet  Google Scholar 

  54. Tsai, C.-H.: An interactive and interpretable interface for diversity in recommender systems. In: Proceedings of the 22nd International Conference on Intelligent User Interfaces Companion, pp. 225–228. ACM, Limassol and Cyprus (2017)

    Google Scholar 

  55. Adomavicius, G., Kwon, Y.: Maximizing aggregate recommendation diversity: a graph-theoretic approach. In: Proceedings of the 1st International Workshop on Novelty and Diversity in Recommender Systems (DiveRS 2011) (2011)

    Google Scholar 

  56. Hwang, S.-Y.H., Hsiung, W.C., Yang, W.S.: A prototype WWW literature recommendation system for digital libraries. Online Inf. Rev. 27(3), 169–182 (2003)

    Article  Google Scholar 

  57. Tsuji, K., et al.: Use of library loan records for book recommendation. In: 2012 IIAI International Conference on Advanced Applied Informatics (IIAIAAI). IEEE (2012)

    Google Scholar 

  58. Middleton, S.E., Shadbolt, N.R., De Roure, D.C.: Ontological user profiling in recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 54–88 (2004)

    Article  Google Scholar 

  59. Jung, S., et al.: SERF: integrating human recommendations with search. In Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management ACM (2004)

    Google Scholar 

  60. Yoshikane, F., Itsumura, H.: Book recommendation based on library loan records and bibliographic information. Procedia-Soc. Behav. Sci. 147, 478–486 (2013)

    Google Scholar 

  61. Pommeranz, A., et al.: Designing interfaces for explicit preference elicitation: a user-centered investigation of preference representation and elicitation process. User Model. User-Adapted Interact. 22(4–5), 357–397 (2012)

    Article  Google Scholar 

  62. Pu, P., Chen, L., Hu, R.: Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Model. User-Adapted Interact. 22(4–5), 317–355 (2012)

    Article  Google Scholar 

  63. Dunn, R., Beaudry, J.S., Klavas, A.: Survey of research on learning styles. California J. Sci. Educ. 2(2), 75–98 (2002)

    Google Scholar 

  64. Swearingen, K., Sinha, R.: Interaction design for recommender systems. In: Designing Interactive Systems (2002)

    Google Scholar 

  65. Kelly, D., Fu, X.: Elicitation of term relevance feedback: an investigation of term source and context. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2006)

    Google Scholar 

  66. Beel, J., et al.: Research paper recommender system evaluation, pp. 15–22 (2013)

    Google Scholar 

  67. Herlocker, J., Jung, S., Webster, J.G.: Collaborative filtering for digital libraries (2012)

    Google Scholar 

  68. Felfernig, A., Burke, R.: Constraint-based recommender systems: technologies and research issues. In: Proceedings of the 10th International Conference on Electronic Commerce. ACM (2008)

    Google Scholar 

  69. Herlocker, J.L., et al.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 5–53 (2004)

    Article  Google Scholar 

  70. Sinha, R., Swearingen, K.: The role of transparency in recommender systems. In: CHI 2002 Extended Abstracts on Human Factors in Computing Systems. ACM (2002)

    Google Scholar 

  71. Beel, J., Langer, S., Nürnberger, A., Genzmehr, M.: The impact of demographics (age and gender) and other user-characteristics on evaluating recommender systems. In: Aalberg, T., Papatheodorou, C., Dobreva, M., Tsakonas, G., Farrugia, C.J. (eds.) TPDL 2013. LNCS, vol. 8092, pp. 396–400. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40501-3_45

    Chapter  Google Scholar 

  72. Gosling, S.D., Rentfrow, P.J., Swann, W.B.: A very brief measure of the big-five personality domains. J. Res. Pers. 37(6), 504–528 (2003)

    Article  Google Scholar 

  73. McCrae, R.R., Costa, P.T.: Validation of the five-factor model of personality across instruments and observers. J. Pers. Soc. Psychol. 52(1), 81 (1987)

    Article  Google Scholar 

  74. Kraaykamp, G., Van Eijck, K.: Personality, media preferences, and cultural participation. Pers. Individ. Differ. 38(7), 1675–1688 (2005)

    Article  Google Scholar 

  75. Rentfrow, P.J., Gosling, S.D.: The do re mi’s of everyday life: the structure and personality correlates of music preferences. J. Pers. Soc. Psychol. 84(6), 1236 (2003)

    Article  Google Scholar 

  76. Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)

    Article  Google Scholar 

  77. Ackerman, M.S., Mainwaring, S.D.: Privacy issues and human-computer interaction. Computer 27(5), 19–26 (2005)

    Google Scholar 

  78. Knijnenburg, B.P., Willemsen, M.C.: The effect of preference elicitation methods on the user experience of a recommender system. In: CHI 2010 Extended Abstracts on Human Factors in Computing Systems. ACM (2010)

    Google Scholar 

  79. Tinschert, J., et al.: Fracture Resistance of Lithium Disilicate–, Alumina-, and Zirconia-based three-unit fixed partial dentures: a laboratory study. Int. J. Prosthodontics 14(3), 231–238 (2001)

    Google Scholar 

  80. Bollen, J., Van de Sompel, H.: An architecture for the aggregation and analysis of scholarly usage data. In: Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries. ACM (2006)

    Google Scholar 

  81. Geyer-Schulz, A., Neumann, A., Thede, A.: An architecture for behavior-based library recommender systems. Inf. Technol. Libraries 22(4), 165–174 (2003)

    MATH  Google Scholar 

  82. Hiesel, P., et al.: A user interface concept for context-aware recommender systems. Mensch und Computer 2016-Tagungsband (2016)

    Google Scholar 

  83. Murphy-Hill, E., Murphy, G.C.: Recommendation delivery. In: Robillard, M.P., Maalej, W., Walker, R.J., Zimmermann, T. (eds.) Recommendation Systems in Software Engineering, pp. 223–242. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-45135-5_9

    Chapter  Google Scholar 

  84. di Sciascio, C.: Advanced user interfaces and hybrid recommendations for exploratory search. In: Proceedings of the 22nd International Conference on Intelligent User Interfaces Companion. ACM (2017)

    Google Scholar 

  85. Luo, J., et al.: A context-aware personalized resource recommendation for pervasive learning. Cluster Comput. 13(2), 213–239 (2010)

    Article  MathSciNet  Google Scholar 

  86. Chen, C.M., Yang, Y.C.: An intelligent mobile location-aware book recommendation system with map-based guidance that enhances problem-based learning in libraries. In: Zeng, Z., Wang, J. (eds.) Advances in Neural Network Research and Applications. LNEE, vol. 67, pp. 853–860. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12990-2_99

    Chapter  Google Scholar 

  87. Biancalana, C., et al.: An approach to social recommendation for context-aware mobile services. ACM Trans. Intell. Syst. Technol. 4(1), 1–31 (2013)

    Article  Google Scholar 

  88. Gómez, S., et al.: Context-aware adaptive and personalized mobile learning delivery supported by UoLmP. J. King Saud Univ. – Comput. Inf. Sci. 26(1), 47–61 (2014)

    Google Scholar 

  89. Li, Q.C., Dong, Z.-H., Li, T.: Research of information recommendation system based on reading behaviour. In: Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming (2008)

    Google Scholar 

  90. Feil, S., et al.: Using gamification to tackle the cold-start problem in recommender systems. In: Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion. ACM (2016)

    Google Scholar 

  91. de Ziesemer, C.A., Müller, L., Silveira, M.S.: Just rate it! gamification as part of recommendation. In: Kurosu, M. (ed.) HCI 2014. LNCS, vol. 8512, pp. 786–796. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07227-2_75

    Chapter  Google Scholar 

  92. Hussain, J., Khan, W.A., Afzal, M., Hussain, M., Kang, B.H., Lee, S.: Adaptive user interface and user experience based authoring tool for recommendation systems. In: Hervás, R., Lee, S., Nugent, C., Bravo, J. (eds.) UCAmI 2014. LNCS, vol. 8867, pp. 136–142. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13102-3_24

    Chapter  Google Scholar 

  93. Nielsen, J.: 10 usability heuristics for user interface design. Nielsen Norman Group, Fremont [Consult. 20 maio 2014]. Disponível na Internet (1995)

    Google Scholar 

  94. Shneiderman, B.: Designing for fun: how can we design user interfaces to be more fun? Interactions 11(5), 48–50 (2004)

    Article  Google Scholar 

  95. Farooq, U., et al.: Design and evaluation of awareness mechanisms in CiteSeer. Inf. Process. Manag. 44(2), 596–612 (2008)

    Article  Google Scholar 

  96. Garrett, J.J.: Elements of User Experience, The User-Centered Design for the Web and Beyond. Pearson Education, London (2010)

    Google Scholar 

  97. Swearingen, K., Sinha, R.: Beyond algorithms: an HCI perspective on recommender systems. In: ACM SIGIR 2001 Workshop on Recommender Systems (2001)

    Google Scholar 

  98. Rim, R., Amin, M.M., Adel, M.: Bayesian networks for user modeling: predicting the user’s preferences. In: 2013 13th International Conference on Hybrid Intelligent Systems (HIS). IEEE (2013)

    Google Scholar 

  99. Chen, L., Tsoi, H.K.: Users’ decision behavior in recommender interfaces: impact of layout design. In: RecSys 2011 Workshop on Human Decision Making in Recommender Systems (2011)

    Google Scholar 

  100. Malone, T.W.: What makes things fun to learn? A study of intrinsically motivating computer games. Pipeline 6(2), 50 (1981)

    Google Scholar 

  101. Viriyakattiyaporn, P., Murphy, G.C.: Challenges in the user interface design of an IDE tool recommender. In: Proceedings of the 2009 ICSE Workshop on Cooperative and Human Aspects on Software Engineering. IEEE Computer Society (2009)

    Google Scholar 

  102. Montaner Rigall, M.: Collaborative recommender agents based on case-based reasoning and trust. Universitat de Girona (2003)

    Google Scholar 

  103. Panniello, U., Gorgoglione, M., Tuzhilin, A.: In CARSWe trust: how context-aware recommendations affect customers’ trust and other business performance measures of recommender systems (2015)

    Google Scholar 

  104. Lim, B.Y.: Improving understanding and trust with intelligibility in context-aware applications. Oregon State University (2012)

    Google Scholar 

  105. Neves, A.R.M., Carvalho, Á.M.G., Ralha, C.G.: Agent-based architecture for context-aware and personalized event recommendation. Expert Syst. Appl. 41(2), 563–573 (2014)

    Article  Google Scholar 

  106. Luo, J., et al.: A context-aware personalized resource recommendation for pervasive learning. Cluster Comput. 13(2), 213–239 (2009)

    Article  MathSciNet  Google Scholar 

  107. Joonseok Lee, K.L., Kim, J.G.: Personalized academic research paper recommendation system. arXiv preprint arXiv:1304.5457 (2013)

  108. Kaminskas, M., Bridge, D.: Measuring surprise in recommender systems/serendipity. In: Proceedings of the Workshop on Recommender Systems Evaluation: Dimensions and Design (Workshop Programme of the 8th ACM Conference on Recommender Systems) (2014)

    Google Scholar 

  109. Reisenzein, R.: The subjective experience of surprise. In: The Message Within: The Role of Subjective Experience in Social Cognition and Behavior, pp. 262–279 (2000)

    Google Scholar 

  110. Kaminskas, M., Bridge, D.: Measuring surprise in recommender systems. In: Proceedings of the Workshop on Recommender Systems Evaluation: Dimensions and Design (Workshop Programme of the 8th ACM Conference on Recommender Systems) (2014)

    Google Scholar 

  111. Cyr, D., Head, M., Ivanov, A.: Design aesthetics leading to m-loyalty in mobile commerce. Inf. Manag. 43(8), 950–963 (2006)

    Article  Google Scholar 

  112. Deaton, M.: The elements of user experience: user-centered design for the Web. Interactions 10(5), 49–51 (2003)

    Article  Google Scholar 

  113. Hekkert, P.: Design aesthetics: principles of pleasure in design. Psychol. Sci. 48(2), 157 (2006)

    Google Scholar 

  114. Karvonen, K.: The beauty of simplicity. In: Proceedings on the 2000 Conference on Universal Usability. ACM (2000)

    Google Scholar 

  115. Anderson, J.C., Narus, J.A.: A model of distributor firm and manufacturer firm working partnerships. J. Market. 54, 42–58 (1990)

    Article  Google Scholar 

  116. Storbacka, K., Strandvik, T., Grönroos, C.: Managing customer relationships for profit: the dynamics of relationship quality. Int. J. Serv. Ind. Manag. 5(5), 21–38 (1994)

    Article  Google Scholar 

  117. Seddon, P., Kiew, M.-Y.: A partial test and development of DeLone and McLean’s model of IS success. Australas. J. Inf. Syst. 4(1) (1996)

    Google Scholar 

  118. Keiningham, T.L., Perkins-Munn, T., Evans, H.: The impact of customer satisfaction on share-of-wallet in a business-to-business environment. J. Serv. Res. 6(1), 37–50 (2003)

    Article  Google Scholar 

  119. Dey, A.K.: Understanding and using context. Pers. Ubiquitous Comput. 5(1), 4–7 (2001)

    Article  MathSciNet  Google Scholar 

  120. McCay-Peet, L., Toms, E.: Measuring the dimensions of serendipity in digital environments. Inf. Res.: Int. Electron. J. 16(n3), 6 (2011)

    Google Scholar 

  121. Kotkov, D., Veijalainen, J., Wang, S.: Challenges of serendipity in recommender systems. In: WEBIST 2016: Proceedings of the 12th International Conference on Web Information Systems and Technologies, vol. 2. SCITEPRESS (2016). ISBN 978-989-758-186-1

    Google Scholar 

  122. Kotkov, D., Wang, S., Veijalainen, J.: A survey of serendipity in recommender systems. Knowl.-Based Syst. 111, 180–192 (2016)

    Article  Google Scholar 

  123. Beel, J., Langer, S.: A comparison of offline evaluations, online evaluations, and user studies in the context of research paper recommender systems (2014, Under Review, Pre-print). http://www.docear.org/publications

  124. Pratley, A., van Voorthuysen, E., Chan, R.: A step-by-step approach for modelling complex systems with Partial Least Squares. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 229, 847–859 (2014)

    Article  Google Scholar 

  125. Hulland, J.: Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strateg. Manag. J. 20(2), 195–204 (1999)

    Article  Google Scholar 

  126. Henseler, J., et al.: Common beliefs and reality about PLS comments on Rönkkö and Evermann. Org. Res. Methods 17, 182–209 (2014)

    Article  Google Scholar 

  127. Sharma, P.N., Kim, K.H.: Model selection in information systems research using partial least squares based structural equation modeling (2012)

    Google Scholar 

  128. Hair Jr., J.F., et al.: Partial least squares structural equation modeling (PLS-SEM) an emerging tool in business research. Eur. Bus. Rev. 26(2), 106–121 (2014)

    Article  Google Scholar 

  129. Urbach, N., Ahlemann, F.: Structural equation modeling in information systems research using partial least squares. J. Inf. Technol. Theory Appl. 11(2), 5–40 (2010)

    Google Scholar 

  130. Hair, J.F., Ringle, C.M., Sarstedt, M.: The use of partial least squares (PLS) to address marketing management topics. J. Market. Theory Pract. 19(2), 135–138 (2011)

    Article  Google Scholar 

  131. Vinzi, V., et al.: Handbook of Partial Least Squares. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-540-32827-8

    Book  MATH  Google Scholar 

  132. Lewis, B.R., Templeton, G.F., Byrd, T.A.: A methodology for construct development in MIS research. Eur. J. Inf. Syst. 14(4), 388–400 (2005)

    Article  Google Scholar 

  133. Moore, G.C., Benbasat, I.: Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf. Syst. Res. 2(3), 192–222 (1991)

    Article  Google Scholar 

  134. Stephenson, W.: The Study of Behavior. Q-Technique and its Methodology. Chicago University Press, Chicago (1953)

    Google Scholar 

  135. Hair, J.F., Ringle, C.M., Sarstedt, M.: Editorial-partial least squares structural equation modeling: rigorous applications, better results and higher acceptance. Long Range Plann. 46(1–2), 1–12 (2013)

    Article  Google Scholar 

  136. Rubens, N., Kaplan, D., Sugiyama, M.: Active learning in recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 735–767. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3_23

    Chapter  Google Scholar 

  137. Hurley, N.J.: Robustness of recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems. ACM (2011)

    Google Scholar 

  138. Sridharan, S.: Introducing serendipity in recommender systems through collaborative methods (2014)

    Google Scholar 

  139. Adamopoulos, P., Tuzhilin, A.: On unexpectedness in recommender systems: or how to expect the unexpected. In: Workshop on Novelty and Diversity in Recommender Systems (DiveRS 2011), at the 5th ACM International Conference on Recommender Systems (RecSys’ 11). ACM, Chicago (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Interactive Arts and Technology, Simon Fraser University Vancouver, Vancouver, Canada

    Zohreh Dehghani Champiri & Brian Fisher

  2. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

    Loo Chu Kiong & Mahmoud Danaee

Authors
  1. Zohreh Dehghani Champiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brian Fisher
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Loo Chu Kiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mahmoud Danaee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zohreh Dehghani Champiri .

Editor information

Editors and Affiliations

  1. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. Aaron Marcus and Associates, Berkeley, CA, USA

    Aaron Marcus

  3. World Usability Day and Bentley User Experience Center, Newton Center, MA, USA

    Elizabeth Rosenzweig

  4. Tsinghua University, Beijing, China

    Pei-Luen Patrick Rau

  5. San Jose State University, San Jose, CA, USA

    Abbas Moallem

  6. Eindhoven Univesity of Technology, Eindhoven, Noord-Brabant, The Netherlands

    Matthias Rauterberg

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Champiri, Z.D., Fisher, B., Kiong, L.C., Danaee, M. (2020). How Contextual Data Influences User Experience with Scholarly Recommender Systems: An Empirical Framework. In: Stephanidis, C., Marcus, A., Rosenzweig, E., Rau, PL.P., Moallem, A., Rauterberg, M. (eds) HCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies. HCII 2020. Lecture Notes in Computer Science(), vol 12423. Springer, Cham. https://doi.org/10.1007/978-3-030-60114-0_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-60114-0_42

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60113-3

  • Online ISBN: 978-3-030-60114-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation