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rScholar: An Interactive Contextual User Interface to Enhance UX of Scholarly Recommender Systems

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HCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies (HCII 2020)

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Abstract

Scholarly recommender systems attempt to reduce the number of research resources or papers presented to scholars and predict the utility of resources for their scholarly tasks. Industry practitioners and academic researchers agree that the interface of a recommender system may have as profound an effect on users’ experience as the recommender’s algorithmic performance. Despite this, little attention has been given to User Interface and Interaction Design of scholarly recommender systems. Scholarly recommender systems rarely use contextual data, such as personal and situational characteristics, that can dramatically affect the user experience (UX) and effectiveness of SRSs. This research presents rScholar, a scholarly recommender system interface that utilizes User Interface and Interaction Design adequacy indicators as well as the user contextual data to enhance the user experience. The evaluation of rScholar is performed by user studies and expert review of feedback by users and by comparison to the UI of the recommendation display of Google Scholar.

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References

  1. 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 

  2. 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 

  3. Murphy-Hill, E., Murphy, Gail C.: Recommendation delivery. In: Robillard, Martin 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 

  4. Nguyen, T.T., et al.: Rating support interfaces to improve user experience and recommender accuracy. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 149–156. ACM, Hong Kong (2013)

    Google Scholar 

  5. Ge, M., Delgado-Battenfeld, C., Jannach, D.: User-perceived recommendation quality-factoring in the user interface (2010)

    Google Scholar 

  6. Abdrabo, W., Wörndl, W.: DiRec: a distributed user interface video recommender. In: IntRS@ RecSys (2016)

    Google Scholar 

  7. Calero Valdez, A., Ziefle, M., Verbert, K.: HCI for recommender systems: the past, the present and the future. In: Proceedings of the 10th ACM Conference on Recommender Systems. ACM (2016)

    Google Scholar 

  8. 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 

  9. 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 

  10. Champiri, Z.D.: A contextual bayesian user experience model for scholarly recommender systems. Doctoral dissertation, University of Malaya (2019)

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  13. McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: CHI’06 Extended Abstracts on Human Factors in Computing Systems. ACM (2006)

    Google Scholar 

  14. Saffer, D.: Designing for Interaction: Creating Innovative Applications and Devices. New Riders, Indianapolis (2010)

    Google Scholar 

  15. Felfernig, A., Burke, R., Pu, P.: Preface to the special issue on user interfaces for recommender systems. User Model. User-Adapt. Interact. 22(4), 313–316 (2012). https://doi.org/10.1007/s11257-012-9120-5

    Article  Google Scholar 

  16. Knijnenburg, B.P., et al.: Explaining the user experience of recommender systems. User Model. User-Adapt. Interact. 22(4–5), 441–504 (2012). https://doi.org/10.1007/s11257-011-9118-4

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  19. Pu, P., Chen, L., Hu, R.: Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Model. User-Adapt. Interact. 22(4), 317–355 (2012). https://doi.org/10.1007/s11257-011-9115-7

    Article  Google Scholar 

  20. 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 

  21. 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 

  22. Knijnenburg, B.P., Willemsen, M.C.: The effect of preference elicitation methods on the user experience of a recommender system. In: CHI’10 Extended Abstracts on Human Factors in Computing Systems. ACM (2010)

    Google Scholar 

  23. McNee, S., et al.: Interfaces for eliciting new user preferences in recommender systems. In: User Modeling 2003, p. 148 (2003)

    Google Scholar 

  24. Cosley, D., et al.: Is seeing believing?: how recommender system interfaces affect users’ opinions. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 585–592. ACM, Ft. Lauderdalep (2003)

    Google Scholar 

  25. Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User-Adap. Inter. 22(1), 101–123 (2012). https://doi.org/10.1007/s11257-011-9112-x

    Article  Google Scholar 

  26. Cremonesi, P., Elahi, M., Garzotto, F.: User interface patterns in recommendation-empowered content intensive multimedia applications. Multimed. Tools Appl. 76(4), 5275–5309 (2016). https://doi.org/10.1007/s11042-016-3946-5

    Article  Google Scholar 

  27. Dehghani Champiri, Z., et al.: A multi-layer contextual model for recommender systems in digital libraries. In: Aslib Proceedings. Emerald Group Publishing Limited (2011)

    Google Scholar 

  28. Champiri, Z.D., Salim, S.S.B., Shahamiri, S.R.: The role of context for recommendations in digital libraries. Int. J. Soc. Sci. Human. 5(11), 948 (2015)

    Article  Google Scholar 

  29. 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 

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

    Google Scholar 

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

    Google Scholar 

  32. 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 2011). ACM, Illinois (2011)

    Google Scholar 

  33. Freund, L., et al.: Exposing and exploring academic expertise with virtu (2010)

    Google Scholar 

  34. Yeung, P.C., Freund, L., Clarke, C.L.: X-site: a workplace search tool for software engineers. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2007)

    Google Scholar 

  35. Hurley, N.J.: Towards diverse recommendation. In: Workshop on Novelty and Diversity in Recommender Systems (DiveRS 2011). Citeseer (2011)

    Google Scholar 

  36. Beenen, G., et al.: Using social psychology to motivate contributions to online communities. In: Proceedings of the 2004 ACM Conference on Computer Supported Cooperative Work (2004)

    Google Scholar 

  37. Pu, P., Chen, L., Hu, R.: Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Model. User-Adap. Inter. 22(4–5), 317–355 (2012). https://doi.org/10.1007/s11257-011-9115-7

    Article  Google Scholar 

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

    Article  Google Scholar 

  39. Pommeranz, A., et al.: Designing interfaces for explicit preference elicitation: a user-centered investigation of preference representation and elicitation process. User Model. User-Adap. Interact. 22(4–5), 357–397 (2012). https://doi.org/10.1007/s11257-011-9116-6

    Article  Google Scholar 

  40. 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 

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

    Google Scholar 

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

    Google Scholar 

  43. 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 

  44. 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 (2017)

    Google Scholar 

  45. 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). Citeseer (2011)

    Google Scholar 

  46. Tintarev, N., Masthoff, J.: A survey of explanations in recommender systems. In: 2007 IEEE 23rd International Conference on Data Engineering Workshop. IEEE (2007)

    Google Scholar 

  47. Kim, J.K., Kim, H.K., Cho, Y.H.: A user-oriented contents recommendation system in peer-to-peer architecture. Expert Syst. Appl. 34(1), 300–312 (2008)

    Article  Google Scholar 

  48. Cosley, D., et al.: Is seeing believing? How recommender system interfaces affect users’ opinions. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2003)

    Google Scholar 

  49. Konstan, J.A.: Introduction to recommender systems: algorithms and evaluation. ACM Trans. Inf. Syst. (TOIS) 22(1), 1–4 (2004)

    Article  Google Scholar 

  50. 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 

  51. Pu, P., Chen, L.: Trust-inspiring explanation interfaces for recommender systems. Knowl.-Based Syst. 20(6), 542–556 (2007)

    Article  Google Scholar 

  52. Vig, J., Sen, S., Riedl, J.: Tagsplanations: explaining recommendations using tags. In: Proceedings of the 14th International Conference on Intelligent User Interfaces (2009)

    Google Scholar 

  53. Lam, S.K.T., Frankowski, D., Riedl, J.: Do you trust your recommendations? An exploration of security and privacy issues in recommender systems. In: Müller, G. (ed.) ETRICS 2006. LNCS, vol. 3995, pp. 14–29. Springer, Heidelberg (2006). https://doi.org/10.1007/11766155_2

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  55. Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User-Adap. Interact. 22(1–2), 101–123 (2012). https://doi.org/10.1007/s11257-011-9112-x

    Article  Google Scholar 

  56. Knijnenburg, B.P., Berkovsky, S.: Privacy for recommender systems: tutorial abstract. In: Proceedings of the Eleventh ACM Conference on Recommender Systems. ACM (2017)

    Google Scholar 

  57. Ramakrishnan, N., et al.: Privacy risks in recommender systems. IEEE Internet Comput. 6, 54–62 (2001)

    Article  Google Scholar 

  58. Kobsa, A., Schreck, J.: Privacy through pseudonymity in user-adaptive systems. ACM Trans. Internet Technol. (TOIT) 3(2), 149–183 (2003)

    Article  Google Scholar 

  59. 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 

  60. Tam, K.Y., Ho, S.Y.: Web personalization as a persuasion strategy: an elaboration likelihood model perspective. Inf. Syst. Res. 16(3), 271–291 (2005)

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  64. Wiegers, K.E.: Peer Reviews in Software: A Practical Guide. Addison-Wesley, Boston (2002)

    Google Scholar 

  65. Dimitrov, D.M., Rumrill Jr., P.D.: Pretest-posttest designs and measurement of change. Work 20(2), 159–165 (2003)

    Google Scholar 

  66. Beel, J., Breitinger, C., Langer, S., Lommatzsch, A., Gipp, B.: Towards reproducibility in recommender-systems research. User Model. User-Adap. Interact. 26(1), 69–101 (2016). https://doi.org/10.1007/s11257-016-9174-x

    Article  Google Scholar 

  67. Shneiderman, B., Plaisant, C.: Designing the user interface: strategies for effective human-computer interaction. Pearson Education India, Delhi (2010)

    Google Scholar 

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

    Google Scholar 

  69. Beel, J., et al.: A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation. In: Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation. ACM (2013)

    Google Scholar 

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Author information

Authors and Affiliations

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

    Zohreh Dehghani Champiri & Brian Fisher

  2. School of Information, University of British Columbia, Vancouver, Canada

    Luanne Freund

Authors
  1. Zohreh Dehghani Champiri
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  2. Brian Fisher
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  3. Luanne Freund
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Corresponding authors

Correspondence to Zohreh Dehghani Champiri , Brian Fisher or Luanne Freund .

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

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Champiri, Z.D., Fisher, B., Freund, L. (2020). rScholar: An Interactive Contextual User Interface to Enhance UX of Scholarly Recommender Systems. 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_43

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  • DOI: https://doi.org/10.1007/978-3-030-60114-0_43

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