Multimedia Tools and Applications

, Volume 76, Issue 4, pp 5275–5309 | Cite as

User interface patterns in recommendation-empowered content intensive multimedia applications

  • Paolo Cremonesi
  • Mehdi ElahiEmail author
  • Franca Garzotto


Design Patterns (DPs) are acknowledged as powerful conceptual tools to improve design quality and to reduce time and cost of the development process by effect of the reuse of “good” design solutions. In many fields (e.g., software engineering, web engineering, interface design) patterns are widely used by practitioners and are also investigated from a research perspective. Still, they have been seldom explored in the arena of Recommender Systems (RSs). RSs provide suggestions (“recommendations”) for items that are likely to be appropriate for the user profile, and are increasingly adopted in content-intensive multimedia applications to complement traditional forms of search in large information spaces. This paper explores RSs through the lens of User Interface (UI) Design Patterns. We have performed a systematic analysis of 54 recommendation-empowered content-intensive multimedia applications, in order to: (i) discover the occurrences of existing domain independent UI patterns; (ii) identify frequently adopted UI solutions that are not modelled by existing patterns, and define a set of new UI patterns, some of which are specific of the interfaces for recommendation features while others can be useful also in a broader context. The results of our inspection have been discussed with and evaluated by a team of experts, leading to a consolidated set of 14 new patterns that are reported in the paper. Reusing pattern-based design solutions instead of building new solutions from scratch enables novice and expert designers to build good UIs for Recommendation-empowered content intensive multimedia applications more effectively, and ultimately can improve the UX experience in this class of systems. From a broader perspective, our work can stimulate future research bridging Recommender Systems, Web Engineering and Interface Design by means of Design Patterns, and highlights new research directions also discussed in the paper.


Multimedia Recommender Systems Design Patterns Human Factors HCI Standardization 



This work has been partially supported by the European Institute of Technology (EIT) - grant EIT DIGITAL # 15008 – 2015.


  1. 1.
    Adomavicius G, Kwon Y (2015) Multi-criteria recommender systems, in: Recommender Systems Handbook, Springer, pp. 847–880Google Scholar
  2. 2.
    Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749CrossRefGoogle Scholar
  3. 3.
    Aggarwal CC (2016) An Introduction to Recommender Systems. In Recommender Systems 2016, pp. 1–28, Springer International PublishingGoogle Scholar
  4. 4.
    Aggarwal CC (2016) Content-Based Recommender Systems. In Recommender Systems, pp. 139–166. Springer International Publishing.Google Scholar
  5. 5.
    Aggarwal CC (2016) Model-Based Collaborative Filtering. In Recommender Systems, pp. 71–138, Springer International PublishingGoogle Scholar
  6. 6.
    Aggarwal CC (2016) Neighborhood-Based Collaborative Filtering. In Recommender Systems, pp. 29–70, Springer International PublishingGoogle Scholar
  7. 7.
    Alexander C (1979) The timeless way of building, vol 1. Oxford University Press, New YorkGoogle Scholar
  8. 8.
    Amatriain X, Basilico J (2015) Recommender Systems in Industry: A Netflix Case Study. In Recommender Systems Handbook, pp. 385–419. Springer USGoogle Scholar
  9. 9.
    Anthony J, Willemsen M C, Felfernig A, Gemmis M D, Lops P, Semeraro G, Chen L (2015) Human decision making and recommender systems. In Recommender Systems Handbook, pp. 611–648. Springer US.Google Scholar
  10. 10.
    Arvola M (2006) Interaction design patterns for computers in sociable use. Int J Comput Appl Technol 25(2–3):128–139. doi: 10.1504/IJCAT.2006.009063 CrossRefGoogle Scholar
  11. 11.
    Balabanovic M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Commun ACM 40(3):66–72CrossRefGoogle Scholar
  12. 12.
    Blas ND, Garzotto F, Guermandi MP (2002) It works! A systematic method to evaluate the features of museum Web-sites, Palazzo dei Congressi, Bibliocom 2002, RomeGoogle Scholar
  13. 13.
    Bollen D, Knijnenburg BP, Willemsen MC, Graus M (2010) Understanding choice overload in recommender systems. In Proceedings of the fourth ACM conference on Recommender Systems, pp. 63–70. ACMGoogle Scholar
  14. 14.
    Borchers JO (2001) A pattern approach to interaction design. Ai Soc 15(4):359–376. doi: 10.1007/BF01206115 MathSciNetCrossRefGoogle Scholar
  15. 15.
    Bottoni P, Guerra E, de Lara J (2010) A language-independent and formal approach to pattern-based modelling with support for composition and analysis. Inf Softw Technol 52(8):821–844CrossRefGoogle Scholar
  16. 16.
    Build Your Perfect Interface with UI Design Patterns. Accessed: 9 April 2015
  17. 17.
    Burke R (2000) Knowledge-based recommender systemsGoogle Scholar
  18. 18.
    Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adapt Interact 12(4):331–370CrossRefzbMATHGoogle Scholar
  19. 19.
    Buschmann F, Meunier R, Rohnert H, Sommerlad P, Stal M (1996) Pattern-oriented software architecture: a system of patterns. Addison WesleyGoogle Scholar
  20. 20.
    Coplien JO, Schmidt DC (1995) Pattern languages of program design. ACM Press/Addison-Wesley Publishing CoGoogle Scholar
  21. 21.
    Cosley D, Lam S K, Albert I, Konstan J A, Riedl J (2003) 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. ACMGoogle Scholar
  22. 22.
    Cremonesi P, Elahi M, Garzotto F (2015) Interaction design patterns in recommender systems, Proceedings of the 11th Biannual Conference on Italian SIGCHI Chapter, pp. 66–73, ACM.Google Scholar
  23. 23.
    Cremonesi P, Garzotto F, Negro S, Papadopoulos A, Turrin R (2011) Comparative evaluation of recommender system quality. In CHI’11 Extended Abstracts on Human Factors in Computing Systems, pp. 1927–1932. ACMGoogle Scholar
  24. 24.
    Cremonesi P, Garzotto F, Negro S, Papadopoulos A, Turrin R (2011) Looking for “good” recommendations: A comparative evaluation of recommender systems. In IFIP Conference on Human-Computer Interaction, pp. 152–168. Springer Berlin HeidelbergGoogle Scholar
  25. 25.
    Cremonesi P, Garzotto F, Turrin R (2012) Investigating the persuasion potential of Recommender Systems from a quality perspective: An empirical study. ACM Transactions on Interactive Intelligent Systems (TiiS) 2, no. 2 pp. 11Google Scholar
  26. 26.
    Cremonesi P, Garzotto F, Turrin R (2012) User effort vs. accuracy in rating-based elicitation. In Proceedings of the sixth ACM conference on Recommender Systems, pp. 27–34. ACMGoogle Scholar
  27. 27.
    Cremonesi P, Garzotto F, Turrin R (2013) User-centric vs. System-centric evaluation of recommender systems. Human-computer interaction–INTERACT 2013. Springer, Berlin Heidelberg, pp 334–351Google Scholar
  28. 28.
    Cremonesi P, Turrin R (2010) Recommender systems for interactive TVGoogle Scholar
  29. 29.
    Cremonesi P, Turrin R (2010) Time-evolution of IPTV recommender systems. In Proceedings of the 8th international interactive conference on Interactive TV&Video, pp. 105–114. ACMGoogle Scholar
  30. 30.
    Dearden A, Finlay J (2006) Pattern languages in HCI: a critical review. Hum–Comput Interact 21(1):49–102CrossRefGoogle Scholar
  31. 31.
    Deldjoo Y, Elahi M, Cremonesi P, Garzotto F, Piazzolla P (2016) Recommending Movies Based on Mise-en-Scene Design. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 1540–1547. ACMGoogle Scholar
  32. 32.
    Deldjoo Y, Elahi M, Cremonesi P, Garzotto F, Piazzolla P, Quadrana M (2016) Content-Based Video Recommendation System Based on Stylistic Visual Features, Journal on Data Semantics, pp. 1–15, SpringerGoogle Scholar
  33. 33.
    Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Trans Inf Syst (TOIS) 22(1):143–177CrossRefGoogle Scholar
  34. 34.
    Design patterns. Accessed: 3 April 2015Google Scholar
  35. 35.
    Desrosiers C, Karypis G (2011) A comprehensive survey of neighborhood-based recommendation methods. In: F. Ricci, L. Rokach, B. Shapira, P.B. Kantor (Eds.), Recommender systems handbook, Springer, pp. 107–144Google Scholar
  36. 36.
    Di Blas N, Garzotto F, Poggi C (2009) Web engineering at the frontier of the Web 2.0: design patterns for online 3D shared spaces. World Wide Web 12(4):345–379. doi: 10.1007/s11280-009-0065-5 CrossRefGoogle Scholar
  37. 37.
    Dong J, Zhao Y, Peng T (2009) A review of design pattern mining techniques. Int J Softw Eng Knowl Eng 19(06):823–855CrossRefGoogle Scholar
  38. 38.
    Duyne DKV, Landay J, Hong JI (2002) The design of sites: patterns, principles, and processes for crafting a customer-centered Web experience. Addison-Wesley Longman Publishing Co., IncGoogle Scholar
  39. 39.
    Elahi M, Ricci F, Rubens N (2016) A survey of active learning in collaborative filtering recommender systems, Computer Science Review, ElsevierGoogle Scholar
  40. 40.
    Garzotto F, Matera M, Paolini P, (1998). To use or not to use? Evaluating usability of museum web sites. In Proceedings of Museums and the Web International Conference, Toronto, Canada, Archives & Museum Informatics, 1998;
  41. 41.
    Felfernig A, Friedrich G, Jannach D, Zanker M (2015) Constrain-tbased recommender systems, in: Recommender Systems Handbook, Springer, pp. 161–190Google Scholar
  42. 42.
    Francis P, Farzin H, Guéhéneuc YG, Moha N (2012) Recommendation system for design patterns in software development: An dpr overview. In Proceedings of the Third International Workshop on Recommendation Systems for Software Engineering, .. 1–5. IEEE PressGoogle Scholar
  43. 43.
    Gamma E, Helm R, Johnson R, Vlissides J (1994) Design patterns: elements of reusable object-oriented software. Pearson EducationGoogle Scholar
  44. 44.
    Garzotto F, Paolini P, Bolchini D, Valenti S (1999) “Modeling-by-Patterns” of Web A.lications. In International Conference on Conceptual Modeling, .. 293–306. Springer Berlin Heidelberg. doi: 10.1007/3-540-48054-4_24Google Scholar
  45. 45.
    Garzotto F, Retalis S (2004) Symposium on design patterns for e-learning. In EDMEDIA 2004, Lugano, SwitzerlandGoogle Scholar
  46. 46.
    Gemmis MD, Lops P, Musto C, Narducci F, Semeraro G (2015) Semantics-aware content-based recommender systems, in: Recommender Systems Handbook, Springer, pp. 119–159Google Scholar
  47. 47.
    Goodyear P, Avgeriou P, Baggetun R, Bartoluzzi S, Retalis S, Ronteltap F, and Rusman E (2004) Towards a pattern language for networked learning. In Networked learning, pp. 449–455Google Scholar
  48. 48.
    Guéhéneuc YG, Mustapha R (2007) A simple recommender system for design patterns. Proceedings of the 1st EuroPLoP Focus Group on Pattern RepositoriesGoogle Scholar
  49. 49.
    Harrison R, Flood D, Duce D (2013) Usability of mobile applications: literature review and rationale for a new usability model. J Interact Sci 1(1):1–16CrossRefGoogle Scholar
  50. 50.
    Hypermedia design patterns repository. Scholar
  51. 51.
    Interaction Design Patterns Library. Accessed: 9 April 2015
  52. 52.
  53. 53.
    Jannach D, Zanker M, Felfernig A, Friedrich G (2010) Recommender systems: an introduction, 1st edn. Cambridge University Press, New YorkCrossRefGoogle Scholar
  54. 54.
    Jézéquel J M, Train M, Mingins C (1999) Design Patterns with Contracts. Addison-Wesley Longman Publishing Co., IncGoogle Scholar
  55. 55.
    Kantor PB, Rokach L, Ricci F, Shapira B (2011) Recommender systems handbook. SpringerGoogle Scholar
  56. 56.
    Koren Y, Bell R (2015) Advances in collaborative filtering, in: Recommender Systems Handbook, Springer, pp. 77–118Google Scholar
  57. 57.
    Liikkanen LA, Salovaara A (2015) Music on YouTube: user engagement with traditional, user-appropriated and derivative videos. Comput Hum Behav 50:108–124CrossRefGoogle Scholar
  58. 58.
    Lockyer L, Bennett S, Agostinho S, Harper B (2009) Handbook of research on learning design and learning objects: issues, applications, and technologies (2 volumes). IGI Global, HersheyCrossRefGoogle Scholar
  59. 59.
    Manzato D, Fonseca NLS (2013) A survey of channel switching schemes for IPTV. Commun Mag, IEEE 51(8):120–127CrossRefGoogle Scholar
  60. 60.
    Martin D, Rodden T, Sommerville I, Rouncefield M, Hughes J (2002) Pointer: Patterns of interaction: A pattern language for CSCW Accessed: 17 December 2008Google Scholar
  61. 61.
    McNee S M, Riedl J, Konstan J A (2006) Being accurate is not enough: how accuracy metrics have hurt recommender systems. In CHI’06 extended abstracts on Human factors in computing systems, pp. 1097–1101. ACMGoogle Scholar
  62. 62.
    Nadia B, Kouas A, Ben-Abdallah H (2011) A design pattern recommendation approach. In Software Engineering and Service Science (ICSESS), 2011 I.E. 2nd International Conference on, pp. 590–593. IEEEGoogle Scholar
  63. 63.
    Nageswara RK (2010) Application domain and functional classification of recommender systems—a survey. DESIDOC J Libr Inf Technol 28(3):17–35CrossRefGoogle Scholar
  64. 64.
    Resnick P, Varian HR (1997) Recommender systems. Commun ACM 40(3):56–58CrossRefGoogle Scholar
  65. 65.
    Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. Springer, US, pp 1–35CrossRefzbMATHGoogle Scholar
  66. 66.
    Richardson JH (2014) The Spotify Paradox: how the creation of a compulsory license scheme for streaming on-demand music platforms can save the music industry. Entertainment Law Review 22, no. 1Google Scholar
  67. 67.
    Rossi G, Schwabe D, Garrido A (1997) Design reuse in hypermedia a.lications development. In Proceedings of the eighth ACM conference on Hypertext, pp. 57–66. ACMGoogle Scholar
  68. 68.
    Rossi G, Schwabe D, Garrido A (1997) Design Reuse in Hypermedia Applications Development. In Proc. of the ACM International Conference on Hypertext ’97, ACM Press, pp. 57–66Google Scholar
  69. 69.
    Rossi G, Schwabe D, Lyardet F (1999) Improving Web Information Systems with Design Patterns. In Proc. of the 8th International World Wide Web Conference, Toronto (CA), Elsevier ScienceGoogle Scholar
  70. 70.
    Rubens N, Elahi M, Sugiyama M, Kaplan D (2015) Active Learning in recommender systems, Recommender Systems Handbook - chapter 24: Recommending Active Learning, pp. 809–846, Springer USGoogle Scholar
  71. 71.
    Schedl M, Knees P, McFee B, Bogdanov D, Kaminskas M (2015) Music Recommender Systems. In Recommender Systems Handbook, pp. 453–492. Springer USGoogle Scholar
  72. 72.
    Schummer T (2003) Gama: A pattern language for computer supported dynamic collaboration. In EuroPLoP, pp. 53–114Google Scholar
  73. 73.
    Schummer T (2005) A pattern approach for end user centered groupware development. Fern Universitat in HagenGoogle Scholar
  74. 74.
    Schummer T, Lukosch S (2013) Patterns for computer-mediated interaction. WileyGoogle Scholar
  75. 75.
    Shvets A, Frey G, M Pavlova (2016) Proxy Design Pattern from Design Patterns Explained Simply,, SourceMaking.comGoogle Scholar
  76. 76.
    Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Advances in artificial intelligence, pp. 4Google Scholar
  77. 77.
    Swearingen K, Sinha R (2001) Beyond algorithms: an HCI perspective on recommender systems. In ACM SIGIR 2001 Workshop on Recommender Systems, vol. 13, no. 5–6, pp. 1–11Google Scholar
  78. 78.
    Tidwell J (2010) Designing interfaces. O’Reilly Media, IncGoogle Scholar
  79. 79.
    Véras D, Prota T, Bispo A, Prudêncio R, Ferraz C (2015) A literature review of recommender systems in the television domain. Expert Syst Appl 42(22):9046–9076CrossRefGoogle Scholar
  80. 80.
    Van Welie M, Van der Veer GC (2003) Pattern languages in interaction design: structure and organization. Proc Interact 3:1–5Google Scholar
  81. 81.
    Welie, M Van (2008) Design patterns for web, gui, and mobile interfaces. Accessed: 17 Dec 2008Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Electronics, Information and BioengineeringPolitecnico di MilanoMilanItaly

Personalised recommendations