Advertisement

Handling Preferences

  • Alexander Felfernig
  • Ludovico Boratto
  • Martin Stettinger
  • Marko Tkalčič
Chapter
Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Abstract

This chapter presents an overview of approaches related to the handling of preferences in (group) recommendation scenarios. We first introduce the concept of preferences and then discuss how preferences can be handled for different recommendation approaches. Furthermore, we sketch how to deal with inconsistencies such as contradicting preferences of individual users.

References

  1. 1.
    G. Adomavicius, J. Bockstedt, C. Shawn, J. Zhang, De-biasing user preference ratings in recommender systems, in Joint Workshop on Interfaces and Human Decision Making for Recommender Systems. CEUR Workshop Proceedings, vol. 1253 (2014), pp. 2–9Google Scholar
  2. 2.
    M. Agarwal, D. Reid, Predicting group choice: an experimental study using conjoint analysis, in Academy of Marketing Science (AMS) Annual Conference (1984), pp. 445–449Google Scholar
  3. 3.
    X. Amatriain, J. Pujol, N. Tintarev, N. Oliver, Rate it again: increasing recommendation accuracy by user re-rating, in 3rd ACM Conference on Recommender Systems, New York, USA (2009), pp. 173–180Google Scholar
  4. 4.
    M. Atas, A. Felfernig, M. Stettinger, T.N. Trang Tran, Beyond item recommendation: using recommendations to stimulate knowledge sharing in group decisions, in 9th International Conference on Social Informatics (SocInfo 2017), Oxford, UK (2017), pp. 368–377Google Scholar
  5. 5.
    J. Bettman, M. Luce, J. Payne, Constructive consumer choice processes. J. Consum. Res. 25(3), 187–217 (1998)CrossRefGoogle Scholar
  6. 6.
    D. Bollen, M. Graus, M. Willemsen, Remembering the stars?: effect of time on preference retrieval from memory, in 6th CM Conference on Recommender Systems (Dublin, Ireland, 2012), pp. 217–220Google Scholar
  7. 7.
    R. Brafman, F. Rossi, D. Salvagnin, K. Venable, T. Walsh, Finding the next solution in constraint- and preference-based knowledge representation formalisms, in 12th International Conference on the Principles of Knowledge Representation and Reasoning (KR 2010), Toronto, Ontario, Canada (2010), pp. 425–433Google Scholar
  8. 8.
    D. Chao, J. Balthorp, S. Forrest, Adaptive radio: achieving consensus using negative preferences, in ACM SIGGROUP Conference on Supporting Group Work, Sanibel Island, FL, USA (2005), pp. 120–123Google Scholar
  9. 9.
    L. Chen, P. Pu, Critiquing-based recommenders: survey and emerging trends. User Model. User-Adap. Inter. 22(1–2), 125–150 (2012)CrossRefGoogle Scholar
  10. 10.
    L. Chen, G. Chen, F. Wang, Recommender systems based on user reviews: the state of the art. User Model. User-Adap. Inter. 25(2), 99–154 (2015)CrossRefGoogle Scholar
  11. 11.
    L. Chen, F. Wang, W. Wu, Inferring users’ critiquing feedback on recommendations from eye movements, in International Conference on Case-Based Reasoning (ICCBR’16), Atlanta, GA, USA (2016), pp. 62–76Google Scholar
  12. 12.
    Y. Chevaleyre, U. Endriss, J. Lang, N. Maudet, A short introduction to computational social choice, in 33rd Conference on Current Trends in Theory and Practice of Computer Science, Harrachov, Czech Republic (2007), pp. 51–69zbMATHGoogle Scholar
  13. 13.
    K. Christakopoulou, F. Radlinski, K. Hofmann, Towards conversational recommender systems, in International Conference on Knowledge Discovery and Data Mining (KDD 2016), San Francisco, CA, USA (2016), pp. 815–824Google Scholar
  14. 14.
    D. Cosley, S. Lam, I. Albert, J. Konstan, J. Riedl, Is seeing believing?: how recommender system interfaces affect users’ opinions, in CHI’03 (2003), pp. 585–592Google Scholar
  15. 15.
    A. Crossen, J. Budzik, K. Hammond, Flytrap: intelligent group music recommendation, in 7th International Conference on Intelligent User Interfaces, San Francisco, CA, USA (2002), pp. 184–185Google Scholar
  16. 16.
    M. De Gemmis, L. Iaquinta, P. Lops, C. Musto, F. Narducci, G. Semeraro, Preference learning in recommender systems, in ECML/PKDD-09 Workshop (2009), pp. 41–55Google Scholar
  17. 17.
    K. Diehl, C. Poynor, Great expectations?! assortment size, expectations, and satisfaction. J. Mark. Res. 47(2), 312–322 (2010)CrossRefGoogle Scholar
  18. 18.
    M.D. Ekstrand, M. Willemsen, Behaviorism is not enough: better recommendations through listening to users, in Proceedings of the 10th ACM Conference on Recommender Systems, RecSys ’16, Boston, MA, USA (ACM, New York, 2016), pp. 221–224CrossRefGoogle Scholar
  19. 19.
    M. Ekstrand, J. Riedl, J. Konstan, Collaborative filtering recommender systems. Found. Trends Hum. Comput. Interact. 4(2), 81–173 (2011)CrossRefGoogle Scholar
  20. 20.
    A. Falkner, A. Felfernig, A. Haag, Recommendation technologies for configurable products. AI Mag. 32(3), 99–108 (2011)CrossRefGoogle Scholar
  21. 21.
    A. Felfernig, R. Burke, Constraint-based recommender systems: technologies and research issues, in ACM International Conference on Electronic Commerce (ICEC08), Innsbruck, Austria (2008), pp. 17–26Google Scholar
  22. 22.
    A. Felfernig, G. Friedrich, D. Jannach, M. Zanker, An integrated environment for the development of knowledge-based recommender applications. Int. J. Electron. Commer. 11(2), 11–34 (2006)CrossRefGoogle Scholar
  23. 23.
    A. Felfernig, B. Gula, E. Teppan, Knowledge-based recommender technologies for marketing and sales. Int. J. Pattern Recognit. Artif. Intell. 21(2), 1–22 (2006). Special Issue of Personalization Techniques for Recommender Systems and Intelligent User InterfacesGoogle Scholar
  24. 24.
    A. Felfernig, G. Friedrich, M. Schubert, M. Mandl, M. Mairitsch, E. Teppan, Plausible repairs for inconsistent requirements, in IJCAI’09, Pasadena, CA (2009), pp. 791–796Google Scholar
  25. 25.
    A. Felfernig, M. Schubert, C. Zehentner, An efficient diagnosis algorithm for inconsistent constraint sets. Artif. Intell. Eng. Des. Anal. Manuf. 26(1), 53–62 (2012)CrossRefGoogle Scholar
  26. 26.
    A. Felfernig, M. Jeran, G. Ninaus, F. Reinfrank, S. Reiterer, M. Stettinger, Basic approaches in recommendation systems. Recommendation Systems in Software Engineering (Springer, Berlin, 2013), pp. 15–37Google Scholar
  27. 27.
    A. Felfernig, M. Schubert, S. Reiterer, Personalized diagnosis for over-constrained problems, in 23rd International Conference on Artificial Intelligence (IJCAI 2013), Peking, China (2013), pp. 1990–1996Google Scholar
  28. 28.
    A. Felfernig, M. Stettinger, G. Leitner, Fostering knowledge exchange using group recommendations, in ACM RecSys’15 Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’15), Vienna, Austria (2015), pp. 9–12Google Scholar
  29. 29.
    A. Felfernig, M. Atas, T.N. Trang Tran, M. Stettinger, Towards group-based configuration, in International Workshop on Configuration 2016 (ConfWS’16) (2016), pp. 69–72Google Scholar
  30. 30.
    A. Felfernig, M. Atas, T.N. Trang Tran, M. Stettinger, S. Polat-Erdeniz, An analysis of group recommendation heuristics for high- and low-involvement items, in International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2017), Arras, France (2017), pp. 335–344Google Scholar
  31. 31.
    P. Grasch, A. Felfernig, F. Reinfrank, ReComment: towards critiquing-based recommendation with speech interaction, in 7th ACM Conference on Recommender Systems (ACM, New York, 2013), pp. 157–164Google Scholar
  32. 32.
    M. Graus, M. Willemsen, Improving the user experience during cold start through choice-based preference elicitation, in Proceedings of the 9th ACM Conference on Recommender Systems, RecSys ’15, Vienna, Austria (ACM, New York, 2015), pp. 273–276Google Scholar
  33. 33.
    D. Grether, C. Plott, Economic theory of choice and the preference reversal phenomenon. Am. Econ. Rev. 69(4), 623–638 (1979)Google Scholar
  34. 34.
    C. Huffman, B. Kahn, Variety for sale: mass customization or mass confusion? J. Retail. 74(4), 491–513 (1998)CrossRefGoogle Scholar
  35. 35.
    A. Jameson, More than the sum of its members: challenges for group recommender systems, in International Working Conference on Advanced Visual Interfaces (2004), pp. 48–54Google Scholar
  36. 36.
    A. Jameson, B. Smyth, Recommendation to groups, in The Adaptive Web, ed. by P. Brusilovsky, A. Kobsa, W. Nejdl. Lecture Notes in Computer Science, vol. 4321 (Springer, New York, 2007), pp. 596–627Google Scholar
  37. 37.
    A. Jameson, S. Baldes, T. Kleinbauer, Two methods for enhancing mutual awareness in a group recommender system, in ACM International Working Conference on Advanced Visual Interfaces, Gallipoli, Italy (2004), pp. 447–449Google Scholar
  38. 38.
    A. Jameson, M. Willemsen, A. Felfernig, M. de Gemmis, P. Lops, G. Semeraro, L. Chen, Human decision making and recommender systems, in Recommender Systems Handbook, 2nd edn., ed. by F. Ricci, L. Rokach, B. Shapira. (Springer, Berlin, 2015), pp. 611–648CrossRefGoogle Scholar
  39. 39.
    D. Jannach, M. Zanker, A. Felfernig, G. Friedrich, Recommender Systems – An Introduction (Cambridge University Press, Cambridge, 2010)CrossRefGoogle Scholar
  40. 40.
    G. Jawaheer, P. Weller, P. Kostkova, Modeling user preferences in recommender systems: a classification framework for explicit and implicit user feedback. ACM Trans. Interact. Intell. Syst. 4(2), 1–26 (2014)CrossRefGoogle Scholar
  41. 41.
    S. Kalloori, F. Ricci, M. Tkalcic, Pairwise preferences based matrix factorization and nearest neighbor recommendation techniques, in Proceedings of the 10th ACM Conference on Recommender Systems, RecSys ’16, New York, NY, USA (ACM, New York, 2016), pp. 143–146Google Scholar
  42. 42.
    D. Kluver, T. Nguyen, M. Ekstrand, S. Sen, J. Riedl, How many bits per rating?, in Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys ’12, New York, NY, USA (ACM, New York, 2012), pp. 99–106CrossRefGoogle Scholar
  43. 43.
    E. Kurdyukova, S. Hammer, E. André, Personalization of content on public displays driven by the recognition of group context. Ambient Intell. 7683, 272–287 (2012)Google Scholar
  44. 44.
    S. Lichtenstein, P. Slovic, The Construction of Preference (Cambridge University Press, Cambridge, 2006)CrossRefGoogle Scholar
  45. 45.
    T. Mahmood, F. Ricci, Improving recommender systems with adaptive conversational strategies, in 20th ACM Conference on Hypertext and Hypermedia, Torino, Italy (2009), pp. 73–82CrossRefGoogle Scholar
  46. 46.
    J. Masthoff, Modeling the multiple people that are me, in User Modeling 2003. Lecture Notes in Artificial Intelligence, vol. 2702 (Springer, Berlin, 2003), pp. 258–262Google Scholar
  47. 47.
    J. Masthoff, Group recommender systems: combining individual models, in Recommender Systems Handbook (Springer, London, 2011), pp. 677–702CrossRefGoogle Scholar
  48. 48.
    J. Masthoff, Group recommender systems: aggregation, satisfaction and group attributes, in Recommender Systems Handbook (Springer, London, 2015), pp. 743–776CrossRefGoogle Scholar
  49. 49.
    J. Masthoff, A. Gatt, In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems. User Model. User-Adap. Inter. 16(3–4), 281–319 (2006)CrossRefGoogle Scholar
  50. 50.
    K. McCarthy, J. Reilly, L. McGinty, B. Smyth, On the dynamic generation of compound critiques in conversational recommender systems, in International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (Springer, Berlin, 2004), pp. 176–184Google Scholar
  51. 51.
    K. McCarthy, L. McGinty, B. Smyth, M. Salamó, Social interaction in the CATS group recommender, in Workshop on the Social Navigation and Community based Adaptation Technologies (2006)Google Scholar
  52. 52.
    D. McFadden, Rationality for economists. J. Risk Uncertain. 19(1–3), 73–105 (1999)CrossRefzbMATHGoogle Scholar
  53. 53.
    B. Miller, I. Albert, S. Lam, J. Konstan, J. Riedl, MovieLens unplugged: experiences with a recommender system on four mobile devices, in People and Computers XVII - Designing for Society, ed. by E. O’Neill, P. Palanque, P. Johnson (Springer, London, 2004), pp. 263–279CrossRefGoogle Scholar
  54. 54.
    J. Neidhardt, L. Seyfang, R. Schuster, H. Werthner, A picture-based approach to recommender systems. Inform. Technol. Tour. 15(1), 49–69 (2015)CrossRefGoogle Scholar
  55. 55.
    T. Nguyen, F. Ricci, A chat-based group recommender system for tourism, in Information and Communication Technologies in Tourism, ed. by R. Schegg, B. Stangl (Springer, Cham, 2017), pp. 17–30Google Scholar
  56. 56.
    J. Payne, J. Bettman, E. Johnson, The Adaptive Decision Maker (Cambridge University Press, Cambridge, 1993)CrossRefGoogle Scholar
  57. 57.
    M. Pazzani, D. Billsus, Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27(3), 313–331 (1997)CrossRefGoogle Scholar
  58. 58.
    B. Peintner, P. Viappiani, N. Yorke-Smith, Preferences in interactive systems: technical challenges and case studies. AI Mag. 29(4), 13–24 (2008)CrossRefGoogle Scholar
  59. 59.
    A. Pommeranz, J. Broekens, P. Wiggers, W. Brinkman, C. Jonker, Designing interfaces for explicit preference elicitation: a user-centered investigation of preference representation and elicitation process. User Model. User-Adap. Inter. 22(4–5), 357–397 (2012)CrossRefGoogle Scholar
  60. 60.
    P. Pu, L. Chen, User-involved preference elicitation for product search and recommender systems. AI Mag. 29(4), 93–103 (2008)CrossRefGoogle Scholar
  61. 61.
    Z. Qiao, P. Zhang, Y. Cao, C. Zhou, L. Guo, Improving collaborative filtering recommendation via location-based user-item subgroup. Proc. Comput. Sci. 29, 400–409 (2014)CrossRefGoogle Scholar
  62. 62.
    L. Quijano-Sanchez, J. Recio-García, B. Díaz-Agudo, G. Jiménez-Díaz, Social factors in group recommender systems. ACM Trans. Intell. Syst. Technol. 4(1), 8:1–8:30 (2006)Google Scholar
  63. 63.
    R. Reiter, A theory of diagnosis from first principles. AI J. 32(1), 57–95 (1987)MathSciNetzbMATHGoogle Scholar
  64. 64.
    F. Ricci, Q. Nguyen, Acquiring and revising preferences in a critique-based mobile recommender systems. IEEE Intell. Syst. 22(3), 22–29 (2007)CrossRefGoogle Scholar
  65. 65.
    P. Sawyer, S. Viller, I. Sommerville, A behavioral model of choice. Q. J. Econ. 69(1), 99–118 (1955)CrossRefGoogle Scholar
  66. 66.
    B. Scheibehenne, R. Greifeneder, P. Todd, Can there ever be too many options? a meta-analytic review of choice overload. J. Consum. Res. 37(3), 409–425 (2010)CrossRefGoogle Scholar
  67. 67.
    B. Schwartz, A. Ward, J. Monterosso, S. Lyubomirsky, K. White, D. Lehman, Maximizing versus satisficing: happiness is a matter of choice. J. Pers. Soc. Psychol. 83(5), 1178–1197 (2002)CrossRefGoogle Scholar
  68. 68.
    E. Sparling, S. Sen, Rating: How difficult is it?, in 5th ACM Conference on Recommender Systems (ACM, New York, 2011), pp. 149–156Google Scholar
  69. 69.
    M. Stettinger, A. Felfernig, G. Leitner, S. Reiterer, Counteracting anchoring effects in group decision making, in 23rd Conference on User Modeling, Adaptation, and Personalization (UMAP’15). Lecture Notes in Computer Science, vol. 9146 (Dublin, Ireland, 2015), pp. 118–130Google Scholar
  70. 70.
    M. Stettinger, A. Felfernig, G. Leitner, S. Reiterer, M. Jeran, Counteracting serial position effects in the Choicla Group decision support environment, in 20th ACM Conference on Intelligent User Interfaces (IUI2015), Atlanta, Georgia, USA (2015), pp. 148–157Google Scholar
  71. 71.
    T. Ulz, M. Schwarz, A. Felfernig, S. Haas, A. Shehadeh, S. Reiterer, M. Stettinger, Human computation for constraint-based recommenders. J. Intell. Inf. Syst. 49(1), 37–57 (2017)CrossRefGoogle Scholar
  72. 72.
    S. Wei, N. Ye, Q. Zhang, Time-aware collaborative filtering for recommender systems, in Chinese Conference on Pattern Recognition, Beijing, China (2012), pp. 663–670Google Scholar
  73. 73.
    C. White, S. Hafenbrädl, U. Hoffrage, N. Reisen, J. Woike, Are groups more likely to defer choice than their members. Judgm. Decis. Mak. 6(3), 239–251 (2011)Google Scholar
  74. 74.
    M. Willemsen, M. Graus, B. Knijnenburg, Understanding the role of latent feature diversification on choice difficulty and satisfaction. User Model. User-Adap. Inter. 26(4), 347–389 (2016)CrossRefGoogle Scholar
  75. 75.
    D. Winterfeldt, W. Edwards, Decision Analysis and Behavioral Research (Cambridge University Press, Cambridge, 1986)Google Scholar
  76. 76.
    H. Xie, J. Lui, Mathematical modeling of group product recommendation with partial information: How many ratings do we need? Perform. Eval. 77, 72–95 (2014)CrossRefGoogle Scholar
  77. 77.
    S. Xu, H. Jiang, F. Lau, Personalized online document, image and video recommendation via commodity eye-tracking, in ACM Conference on Recommender Systems (RecSys’08), Lausanne, Switzerland (2008), pp. 83–90Google Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  • Alexander Felfernig
    • 1
  • Ludovico Boratto
    • 2
  • Martin Stettinger
    • 1
  • Marko Tkalčič
    • 3
  1. 1.Institute for Software TechnologyGraz University of TechnologyGrazAustria
  2. 2.EURECATCentre Tecnológico de CatalunyaBarcelonaSpain
  3. 3.Faculty of Computer ScienceFree University of Bozen-BolzanoBolzanoItaly

Personalised recommendations