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ART: group recommendation approaches for automatically detected groups

  • Ludovico Boratto
  • Salvatore Carta
Original Article

Abstract

Group recommender systems provide suggestions when more than a person is involved in the recommendation process. A particular context in which group recommendation is useful is when the number of recommendation lists that can be generated is limited (i.e., it is not possible to suggest a list of items to each user). In such a case, grouping users and producing recommendations to groups becomes necessary. None of the approaches in the literature is able to automatically group the users in order to overcome the previously presented limitation. This paper presents a set of group recommender systems that automatically detect groups of users by clustering them, in order to respect a constraint on the maximum number of recommendation lists that can be produced. The proposed systems have been largely evaluated on two real-world datasets and compared with hundreds of experiments and statistical tests, in order to validate the results. Moreover, we introduce a set of best practices that help in the development of group recommender systems in this context.

Keywords

Group recommendation Clustering Group modeling 

References

  1. 1.
    Amatriain X, Jaimes A, Oliver N, Pujol JM (2011) Data mining methods for recommender systems. In: Recommender Systems Handbook. Springer, Boston, pp. 39–71Google Scholar
  2. 2.
    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):687–714CrossRefGoogle Scholar
  3. 3.
    Baatarjav EA, Phithakkitnukoon S, Dantu R (2008) Group recommendation system for facebook. In: Meersman R, Tari Z, Herrero P (eds) On the Move to Meaningful Internet Systems: OTM 2008 Workshops, vol 5333. Lecture Notes in Computer Science. Springer, Berlin Heidelberg, pp 211–219Google Scholar
  4. 4.
    Baltrunas L, Makcinskas T, Ricci F (2010) Group recommendations with rank aggregation and collaborative filtering. In: Proceedings of the 2010 ACM Conference on Recommender Systems., RecSys 2010ACM, New York, NY, USA, pp 119–126Google Scholar
  5. 5.
    Bellman RE (1961) Adaptive control processes—a guided tour. Princeton University Press, PrincetonzbMATHGoogle Scholar
  6. 6.
    Boratto L, Carta S (2011) State-of-the-art in group recommendation and new approaches for automatic identification of groups. In: Information Retrieval and Mining in Distributed Environments, vol 324. Studies in Computational Intelligence. Springer, Berlin Heidelberg, pp 1–20Google Scholar
  7. 7.
    Boratto L, Carta S, Chessa A, Agelli M, Clemente ML (2009) Group recommendation with automatic identification of users communities. In: Proceedings of the 2009 IEEE/WIC/ACM International Conference on Web Intelligence and International Conference on Intelligent Agent Technology Workshops. IEEE, pp 547–550Google Scholar
  8. 8.
    Boratto L, Carta S, Satta M (2010) Groups identification and individual recommendations in group recommendation algorithms. In: Practical Use of Recommender Systems, Algorithms and Technologies 2010, CEUR Workshop Proceedings, vol 676Google Scholar
  9. 9.
    Bourke S, McCarthy K, Smyth B (2011) Using social ties in group recommendation. In: AICS 2011: Proceedings of the 22nd Irish Conference on Artificial Intelligence and Cognitive Science: 31 August-2 September, 2011: University of Ulster-Magee. Intelligent Systems Research CentreGoogle Scholar
  10. 10.
    Carolis BD, Pizzutilo S (2009) Providing relevant background information in smart environments. In: E-Commerce and Web Technologies, 10th International Conference, EC-Web 2009. Proceedings, Lecture Notes in Computer Science, vol 5692. Springer, pp 360–371 (2009)Google Scholar
  11. 11.
    Carvalho LAM, Macedo HT (2013) Generation of coalition structures to provide proper groups’ formation in group recommender systems. In: Proceedings of the 22nd International Conference on World Wide Web Companion, WWW ’13 Companion. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, pp. 945–950 (2013)Google Scholar
  12. 12.
    Chen Y, Pu P (2012) Cofeel: emotional social interface in group recommender systems. In: Proceedings of the RecSys’12 Workshop on Interfaces for Recommender Systems, in conjunction with the 6th ACM Conference on Recommender Systems (RecSys’12), pp. 48–55Google Scholar
  13. 13.
    Chen Y, Pu P (2013) Cofeel: using emotions to enhance social interaction in group recommender systems. In: Alpine Rendez-Vous (ARV) 2013 Workshop on Tools and Technology for Emotion-Awareness in Computer Mediated Collaboration and LearningGoogle Scholar
  14. 14.
    Christensen IA, Schiaffino SN (2011) Entertainment recommender systems for group of users. Expert Syst Appl 38(11):14127–14135Google Scholar
  15. 15.
    Crossen A, Budzik J, Hammond KJ (2002) Flytrap: intelligent group music recommendation. In: IUI, pp 184–185Google Scholar
  16. 16.
    Desrosiers C, Karypis G (2011) A comprehensive survey of neighborhood-based recommendation methods. In: Recommender Systems Handbook. Springer, Berlin, pp 107–144Google Scholar
  17. 17.
    Felfernig A, Zehentner C, Ninaus G, Grabner H, Maalej W, Pagano D, Weninger L, Reinfrank F (2012) Group decision support for requirements negotiation. In: Advances in User Modeling—UMAP 2011 Workshops, Revised Selected Papers, Lecture Notes in Computer Science, vol 7138. Springer, New York, pp 105–116 (2012)Google Scholar
  18. 18.
    Herlocker J, Konstan J, Borchers A, Riedl J (1999) An algorithmic framework for performing collaborative filtering. In: Research and Development in Information Retrieval. American Association of Computing Machinery, American Association of Computing MachineryGoogle Scholar
  19. 19.
    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, AVI 2004. ACM Press, pp 48–54Google Scholar
  20. 20.
    Jameson A, Baldes S, Kleinbauer T (2004) Two methods for enhancing mutual awareness in a group recommender system. In: Proceedings of the working conference on Advanced visual interfaces, AVI 2004. ACM Press, pp 447–449 (2004)Google Scholar
  21. 21.
    Jameson A, Smyth B (2007) Recommendation to groups. The Adaptive Web, vol 4321. Methods and Strategies of Web Personalization, Lecture Notes in Computer Science. Springer, Berlin, pp 596–627Google Scholar
  22. 22.
    Jung JJ (2012) Attribute selection-based recommendation framework for short-head user group: an empirical study by movielens and imdb. Expert Syst Appl 39(4):4049–4054. doi: 10.1016/j.eswa.2011.09.096 CrossRefGoogle Scholar
  23. 23.
    Kanungo T, Mount DM, Netanyahu NS, Piatko CD, Silverman R, Wu AY (2002) An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intell 24:881–892. doi: 10.1109/TPAMI.2002.1017616 CrossRefGoogle Scholar
  24. 24.
    Lieberman H, Dyke NWV, Vivacqua AS (1999) Let’s browse: a collaborative web browsing agent. In: IUI, pp 65–68Google Scholar
  25. 25.
    MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, vol 1. University of California Press, pp 281–297Google Scholar
  26. 26.
    Manca M, Boratto L, Carta S (2014) Design and architecture of a friend recommender system in the social bookmarking domain. Proc Sci Inf Conf 2014:838–842Google Scholar
  27. 27.
    Manca M, Boratto L, Carta S (2014) Mining user behavior in a social bookmarking system— a delicious friend recommender system. In: Proceedings of the 3rd International Conference on Data Management Technologies and Applications (DATA 2014), pp 331–338Google Scholar
  28. 28.
    Manca M, Boratto L, Carta S (2015) Friend recommendation in a social bookmarking system: Design and architecture guidelines. In: Arai K, Kapoor S, Bhatia R (eds) Intelligent Systems in Science and Information 2014, Studies in Computational Intelligence, vol 591. Springer, New York, pp 227–242 (2015)Google Scholar
  29. 29.
    Masthoff J (2002) Modeling a group of television viewers. In: Proceedings of the Future TV: Adaptive Instruction In Your Living Room (A workshop for ITS 2002)Google Scholar
  30. 30.
    Masthoff J (2004) Group modeling: selecting a sequence of television items to suit a group of viewers. User Model User-Adapted Interact 14(1):37–85. doi: 10.1023/B:USER.0000010138.79319.fd CrossRefGoogle Scholar
  31. 31.
    Masthoff J (2005) The pursuit of satisfaction: affective state in group recommender systems. In: Proceedings of the 10th international conference on User Modeling. UM’05, Springer-Verlag, Berlin, Heidelberg, pp 297–306Google Scholar
  32. 32.
    Masthoff J (2008) Group adaptation and group modelling. In: Intelligent Interactive Systems in Knowledge-Based Environments, Studies in Computational Intelligence, vol 104. Springer, New York, pp 157–173Google Scholar
  33. 33.
    Masthoff J (2011) Group recommender systems: combining individual models. In: Recommender Systems Handbook. Springer, New York, pp 677–702Google Scholar
  34. 34.
    Masthoff J, Gatt A (2006) In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems. User Model User-Adapted Interact 16(3–4):281–319. doi: 10.1007/s11257-006-9008-3 CrossRefGoogle Scholar
  35. 35.
    McCarthy JF (2002) Pocket restaurantfinder: a situated recommender system for groups. In: Workshop on Mobile Ad-Hoc Communication at the 2002 ACM Conference on Human Factors in Computer Systems. MinneapolisGoogle Scholar
  36. 36.
    McCarthy JF, Anagnost TD (1998) Musicfx: an arbiter of group preferences for computer supported collaborative workouts. In: CSCW ’98, Proceedings of the ACM 1998 Conference on Computer Supported Cooperative Work. ACM, pp 363–372Google Scholar
  37. 37.
    McCarthy K, Salamó M, Coyle L, McGinty L, Smyth B, Nixon P (2006) Cats: a synchronous approach to collaborative group recommendation. In: Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference. AAAI Press, pp 86–91Google Scholar
  38. 38.
    McCarthy K, Salamó M, Coyle L, McGinty L, Smyth B, Nixon P (2006) Group recommender systems: a critiquing based approach. In: Proceedings of the 2006 International Conference on Intelligent User Interfaces. ACM, pp 267–269Google Scholar
  39. 39.
    Ntoutsi E, Stefanidis K, Nørvåg K, Kriegel HP (2012) Fast group recommendations by applying user clustering. In: Proceedings of Conceptual Modeling—31st International Conference ER 2012. Lecture Notes in Computer Science, vol 7532. Springer, New York, pp 126–140Google Scholar
  40. 40.
    Ntoutsi I, Stefanidis K, Nørvåg K, Kriegel HP (2012) grecs: A group recommendation system based on user clustering. In: Database Systems for Advanced Applications—17th International Conference, DASFAA 2012, Proceedings, Part II, Lecture Notes in Computer Science, vol 7239. Springer, pp 299–303Google Scholar
  41. 41.
    O’Connor M, Cosley D, Konstan JA, Riedl J (2001) Polylens: a recommender system for groups of users. In: Proceedings of the Seventh European Conference on Computer Supported Cooperative Work. Kluwer, pp 199–218Google Scholar
  42. 42.
    Padmanabhan V, Seemala SK, Bhukya WN (2011) A rule based approach to group recommender systems. In: Proceedings of the 5th International Conference on Multi-Disciplinary Trends in Artificial Intelligence., MIWAI’11. Springer-Verlag, Berlin, Heidelberg, pp 26–37Google Scholar
  43. 43.
    Pessemier T, Dooms S, Martens L (2013) Comparison of group recommendation algorithms. Multimed Tools Appl 1–45. doi: 10.1007/s11042-013-1563-0
  44. 44.
    Ricci F (2014) Recommender systems: models and techniques. In: Encyclopedia of Social Network Analysis and Mining. Springer, New York, pp 1511–1522Google Scholar
  45. 45.
    Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. In: Recommender Systems Handbook. Springer, Berlin, pp 1–35Google Scholar
  46. 46.
    Sánchez LQ, Bridge DG, Díaz-Agudo B, Recio-García JA (2012) A case-based solution to the cold-start problem in group recommenders. In: Proceedings of Case-Based Reasoning Research and Development—20th International Conference, ICCBR 2012. Lecture Notes in Computer Science, vol 7466. Springer, New York, pp 342–356Google Scholar
  47. 47.
    Schafer JB, Frankowski D, Herlocker JL, Sen S (2007) Collaborative filtering recommender systems. In: The Adaptive Web, Methods and Strategies of Web Personalization, Lecture Notes in Computer Science, vol 4321. Springer, New York, pp 291–324Google Scholar
  48. 48.
    Senot C, Kostadinov D, Bouzid M, Picault J, Aghasaryan A (2011) Evaluation of group profiling strategies. In: IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence. IJCAI/AAAI, pp 2728–2733Google Scholar
  49. 49.
    Senot C, Kostadinov D, Bouzid M, Picault J, Aghasaryan A, Bernier C (2010) Analysis of strategies for building group profiles. In: User Modeling, Adaptation, and Personalization, 18th International Conference, UMAP 2010. Proceedings, Lecture Notes in Computer Science, vol 6075. Springer, New York, pp 40–51Google Scholar
  50. 50.
    Zha ZJ, Tian Q, Cai J, Wang Z (2013) Interactive social group recommendation for flickr photos. Neurocomput 105:30–37. doi: 10.1016/j.neucom.2012.06.039 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Dipartimento di Matematica e InformaticaUniversità di CagliariCagliariItaly

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