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A chat-based group recommender system for tourism

Abstract

Group recommender systems are information filtering and decision support applications that are aimed at aiding a group of users in making decisions when they are considering a set of alternatives. State of the art solutions aggregate users’ preferences acquired before the actual decision making process and suggest items that fit the aggregated model. However, it has been shown that the recommendation needs of groups go beyond the identification of such items, and it is essential to take into account, in the recommendation process, the dynamic of users’ interactions in their real group context. In this paper, we therefore illustrate a novel approach to group recommendation, which is implemented in a mobile system, that monitors and exploits users’ interactions during a group discussion, and offers appropriate recommendations as well as other types of suggestions, to guide and help the group members in settling on an agreement. We have carried out a preliminary user study of the proposed approach to assess and analyze the usability of the system along with the perceived recommendation quality and choice satisfaction. The results of this study are encouraging as they show that the proposed approach attains a high usability score, and has good user-perceived recommendation quality as well as choice satisfaction.

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Notes

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    LTS: http://www.lts.it.

References

  1. Ardissono L, Goy A, Petrone G, Segnan M, Torasso P (2003) Intrigue: personalized recommendation of tourist attractions for desktop and hand held devices. Appl Artif Intell 17(8–9):687–714

    Article  Google Scholar 

  2. Baltrunas L, Makcinskas T, Ricci F (2010) Group recommendations with rank aggregation and collaborative filtering. In: Proceedings of the fourth ACM conference on Recommender systems, pp 119–126

  3. Bangor A, Kortum PT, Miller JT (2008) An empirical evaluation of the system usability scale. Int J Hum Comput Interact 24(6):574–594

    Article  Google Scholar 

  4. Bekkerman P, Kraus S, Ricci F (2006) Applying cooperative negotiation methodology to group recommendation problem. In: Proceedings of workshop on recommender systems in 17th European conference on artificial intelligence (ECAI’06), pp 72–75

  5. Berkovsky S, Freyne J (2010) Group-based recipe recommendations: analysis of data aggregation strategies. In: Proceedings of the fourth ACM conference on recommender systems, pp 111–118

  6. Boratto L, Carta S (2015) The rating prediction task in a group recommender system that automatically detects groups: architectures, algorithms, and performance evaluation. J Intell Inf Syst 45(2):221–245

    Article  Google Scholar 

  7. Braunhofer M, Elahi M, Ricci F, Schievenin T (2013) Context-aware points of interest suggestion with dynamic weather data management. Inf Commun Technol Tour 2014:87–100

    Google Scholar 

  8. Bray RM, Kerr NL, Atkin RS (1978) Effects of group size, problem difficulty, and sex on group performance and member reactions. J Pers Soc Psychol 36(11):1224

    Article  Google Scholar 

  9. Bridge D, Göker MH, McGinty L, Smyth B (2005) Case-based recommender systems. Knowl Eng Rev 20(03):315–320

    Article  Google Scholar 

  10. Chen L, Pu P (2009) Interaction design guidelines on critiquing-based recommender systems. User Model User Adapt Interact 19(3):167–206

    Article  Google Scholar 

  11. Chen L, de Gemmis M, Felfernig A, Lops P, Ricci F, Semeraro G (2013) Human decision making and recommender systems. ACM Trans Interact Intell Syst (TiiS) 3(3):17

    Google Scholar 

  12. Christensen I, Schiaffino S, Armentano M (2016) Social group recommendation in the tourism domain. J Intell Inf Syst 47(2):209–231

    Article  Google Scholar 

  13. Delic A, Neidhardt J (2017) A comprehensive approach to group recommendations in the travel and tourism domain. In: Adjunct publication of the 25th conference on user modeling, adaptation and personalization, pp 11–16

  14. Delic A, Neidhardt J, Nguyen TN, Ricci F (2016a) Research methods for group recommender systems. In: Proceedings of RecTour 2016

  15. Delic A, Neidhardt J, Nguyen TN, Ricci F, Rook L, Werthner H, Zanker M (2016b) Observing group decision making processes. In: Proceedings of the 10th ACM conference on recommender systems, pp 147–150

  16. Delic A, Neidhardt J, Rook L, Werthner H, Zanker M (2017) Researching individual satisfaction with group decisions in tourism: experimental evidence. Inf Commun Technol Tour 2017:73–85

    Google Scholar 

  17. Forsyth DR (2014) Group dynamics, 6th edn. Wadsworth Cengage Learning, Boston

    Google Scholar 

  18. Garcia I, Sebastia L, Onaindia E, Guzman C (2009) A group recommender system for tourist activities. In: International conference on electronic commerce and web technologies, pp 26–37

  19. Guzzi F, Ricci F, Burke R (2011) Interactive multi-party critiquing for group recommendation. In: Proceedings of the fifth ACM conference on recommender systems, pp 265–268

  20. Herzog D (2017) Recommending a sequence of points of interest to a group of users in a mobile context. In: Proceedings of the eleventh ACM conference on recommender systems, pp 402–406

  21. Jameson A (2004) More than the sum of its members: challenges for group recommender systems. In: Proceedings of the working conference on advanced visual interfaces, pp 48–54

  22. Jameson A, Smyth B (2007) Recommendation to groups. Adapt Web LNCS 4321:596–627

    Article  Google Scholar 

  23. Knijnenburg BP, Reijmer NJ, Willemsen MC (2011) 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

  24. Knijnenburg BP, Willemsen MC, Gantner Z, Soncu H, Newell C (2012) Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction 22(4–5):441–504

    Article  Google Scholar 

  25. Lorenzi F, Ricci F (2005) Case-based recommender systems: A unifying view. In: Intelligent Techniques for Web Personalization, pp 89–113

  26. Mahmood T, Ricci F, Venturini A (2009) Learning adaptive recommendation strategies for online travel planning. Inf Commun Technol Tour 2009:149–160

    Google Scholar 

  27. Márquez JOÁ, Ziegler J (2016) Hootle+: a group recommender system supporting preference negotiation. In: CYTED-RITOS international workshop on groupware, pp 151–166

  28. Masthoff J (2004) Group modeling: selecting a sequence of television items to suit a group of viewers. Personalized digital television, pp 93–141

  29. Masthoff J (2015) Group recommender systems: aggregation, satisfaction and group attributes. In: recommender systems handbook, pp 743–776

  30. McCarthy K, Salamó M, Coyle L, McGinty L, Smyth B, Nixon P (2006) Cats: A synchronous approach to collaborative group recommendation. In: Florida Artificial Intelligence Research Society conference, pp 86–91

  31. McGinty L, Reilly J (2011) On the evolution of critiquing recommenders. In: recommender systems handbook, pp 419–453

  32. McGinty L, Smyth B (2002) Comparison-based recommendation. In: European conference on case-based reasoning, pp 575–589

  33. Nguyen TN, Ricci F (2017) A chat-based group recommender system for tourism. Inf Commun Technol Tour 2017:17–30

    Google Scholar 

  34. Nguyen TN, Ricci F (2017b) Combining long-term and discussion-generated preferences in group recommendations. In: Proceedings of the 25th conference on user modeling, adaptation and personalization

  35. Nguyen TN, Ricci F (2017c) Dynamic elicitation of user preferences in a chat-based group recommender system. In: Proceedings of the 32nd ACM symposium on applied computing, pp 1685–1692

  36. Ricci F, Rokach L, Shapira B (2015) Recommender systems: introduction and challenges. In: Recommender systems handbook, pp 1–34

  37. Sauro J, Lewis JR (2012) Quantifying the user experience: practical statistics for user research. Elsevier, Amsterdam

    Google Scholar 

  38. Shafir E, Simonson I, Tversky A (1993) Reason-based choice. Cognition 49(1–2):11–36

    Article  Google Scholar 

  39. Stettinger M, Felfernig A, Leitner G, Reiterer S, Jeran M (2015) Counteracting serial position effects in the CHOICLA group decision support environment. In: Proceedings of the 20th international conference on intelligent user interfaces, pp 148–157

  40. Sylejmani K, Dorn J, Musliu N (2017) Planning the trip itinerary for tourist groups. Information technology and tourism, pp 1–40

  41. Trabelsi W, Wilson N, Bridge D, Ricci F (2010) Comparing approaches to preference dominance for conversational recommenders. In: Proceedings of the 22nd IEEE international conference on tools with artificial intelligence, pp 113–120

  42. Vroom VH, Jago AG (2007) The role of the situation in leadership. Am Psychol 62(1):17

    Article  Google Scholar 

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Correspondence to Thuy Ngoc Nguyen.

Additional information

This paper is an extended version of our conference paper with the same title previously published in the proceedings of Information and Communication Technologies in Tourism 2017 (ENTER 2017) which took place in Rome, Italy, January 24–26, 2017.

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Nguyen, T.N., Ricci, F. A chat-based group recommender system for tourism. Inf Technol Tourism 18, 5–28 (2018). https://doi.org/10.1007/s40558-017-0099-y

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Keywords

  • Group recommender systems
  • Group decision support systems
  • Travel recommender systems