Abstract
This chapter deals with adaptation of background information and advertisements, displayed in an environment, to the interests of the group of people present. According to research on computational advertising, it is important to develop methods for finding the “best match” between user interests in a given context and available advertisements. Accordingly, after providing an overview of the most popular group recommender approaches, this chapter looks at new issues that arise when considering group modeling in pervasive advertising conveyed through digital displays. The chapter first discusses general issues concerning group recommender systems, with particular emphasis on the acquisition of user preferences and interests. A system called GAIN (Group Adaptive Information and News) is then presented. This was developed with the aim of tailoring the display of background information and advertisements to groups of people.
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Notes
- 1.
A.S.D. BodyEnergy, Mola di Bari, Italy.
- 2.
We wanted to express the confidence of each topic as a percentage. For this reason, we set P PROBABLE = f(PSURE), f being a function that relates these two values to one another.
- 3.
This is valid if PSURE + PPROBABLE = 1, otherwise, it is necessary to divide the value of Cj by the value of PSURE + PPROBABLE in order to obtain a result between 0 and 1.
References
Adomavicius, G., Tuzhilin, A.: 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), 734–749 (2005)
Ardissono, L., Goy, A., Petrone, G., Segnan, M., Torasso, P.: INTRIGUE: personalized recommendation of tourist attractions for desktop and handset devices. Appl. Artif. Intell. 17(8–9), 687–714 (2003)
Broder, A., Fontoura, M., Josifovski, V., Riedel, L.: A semantic approach to contextual advertising. In: Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’07), pp. 559–566. ACM, New York (2007)
Burke, R.: Knowledge-based recommender systems. In: Kent, A. (ed.) Encyclopedia of Library and Information Systems, vol. 69, pp. 180–200. Marcel Dekker, New York (2000)
Burke, R.: Hybrid web recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. LNCS, vol. 4321. Springer-Verlag, Berlin/Heidelberg/New York (2007)
Chao, D., Balthrop, J., Forrest, S.: Adaptive radio: achieving consensus using negative preferences. In: Proceedings of the 2005 International ACM SIGGROUP Conference on Supporting Group Work, Sanibel Island, 2005, pp. 120–123
Crossen, A., Budzik, J., Hammond, K.: Flytrap: intelligent group music recommendation. In: Gil, Y., Leake, D. (eds.) IUI 2002: International Conference on Intelligent User Interfaces, pp. 184–185. ACM, New York (2002)
De Carolis, B., Pizzutilo, S.: Providing relevant background information in smart environments. In: Proceedings of EC-Web 2009 LNCS. E-Commerce Web Technol. 5692, 357–362 (2009)
Elderez, S.: Information encountering: a conceptual framework for accidental information discovery. In: Proceedings of an International Conference on Information Seeking in Context (ISIC), Tampere, 1997, pp. 412–421
Endrei, M., et al.: Patterns: Service-Oriented Architecture and Web Services. IBM Redbook (2004)
Heckmann, D.: Ubiquitous User Modeling. IOS Press, New York (2005)
Herlocker, J., Konstan, J., Riedl, J.: Explaining collaborative filtering recommendations. In: Proceedings of the 2000 Conference on Computer-Supported Cooperative Work, Philadelphia, 2000, pp. 241–250
Jameson, A.: More than the sum of its members: challenges for group recommender systems. In: Proceedings of the Working Conference on Advanced Visual interfaces, Gallipoli, 25–28 May 2004
Joachims, T., et al.: Accurately interpreting clickthrough data as implicit feedback, In: Proceedings of the 28th Annual international ACM SIGIR Conference on Research and Development in information Retrieval, Salvador, pp. 154–161. ACM, New York (2005)
Kobsa, A., Koenemann, J., Pohl, W.: Personalized hypermedia presentation techniques for improving online customer relationships. Knowl. Eng. Rev. 16(2), 111–155 (2001)
Maglio, P.P., Campbell, C.S.: Tradeoffs in displaying peripheral information. In: Proceedings of Association for Computing Machinery’s Human Factors in Computing Systems CHI 2000, The Hague, pp. 241–248
Marreiros, G., Ramos, C., Neves, J.: Multiagent approach to group decision making through persuasive argumentation. In: International Conference on Argumentation, Liverpool (2006)
Masthoff, J.: Group modeling: selecting a sequence of television items to suit a group of viewers. User Model. User Adapt. Interact. 14(1), 37–85 (2004)
McCarthy, J., Anagnost, T.: MusicFX: an arbiter of group preferences for computer supported collaborative workouts. In: Proceedings of the ACM 1998 conference on CSCW, Seattle, 1998, pp. 363–372
McCarthy, J.: Pocket restaurantFinder: a situated recommender system for groups. In: Proceedings of the Workshop on Mobile Ad-Hoc Communication at the 2002 ACM Conference on Human Factors in Computer Systems, Minneapolis (2002)
McCarthy, K., Salamo’, M., McGinty, L., Smyth, B.: CATS: a synchronous approach to collaborative group recommendation. In: Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference, (FLAIRS-06), Melbourne Beach, 2006, pp. 86–91
McCarthy, K., et al.: Group recommender systems: a critiquing-based approach. In: Paris, C., Sidner, C. (eds.) Proceedings of IUI 2006: International Conference on Intelligent User Interfaces, pp. 267–269. ACM, New York (2006)
O’Conner, M., Cosley. D., Konstanm, J.A., Riedl, J.: PolyLens: a recommender system for groups of users. In: Proceedings of ECSCW 2001, Bonn, 2001, pp. 199–218
Partridge, K., Begole, B.: Activity-based advertising. In: Müller, J., Alt, F., Michelis, D. (eds.) Pervasive Advertising, pp. 83–102. Springer, Dordrecht (2011)
Pazzani, M.: A framework for collaborative content-based and demographic filtering. Art. Intell. Rev. 13, 393–408 (1999)
Pazzani, J., Billsus, D.: Content-based recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. Lecture Notes in Computer Science, vol. 4321. Springer-verlag, Berlin/Heidelberg/New York (2007)
Ricci, F., Nguyen, Q.N.: Mobyrek: a conversational recommender system for on-the-move travelers. In: Fesenmaier, D.R., Werthner, H., Wober, K.W. (eds.) Destination Recommendation Systems: Behavioural Foundations and Applications, pp. 281–294. CABI Publishing, London (2006)
Schrammel, J., et al.: Attentional Behavior of Users on the Move Towards Pervasive Advertising Media. In: Müller, J., Alt, F., Michelis, D. (eds.) Pervasive Advertising, pp. 287–308. Springer, Dordrecht (2011)
Warnestal, P.: User evaluation of a conversational recommender system. In: IJCAI Workshop on K&R in Practical Dialog Systems, Edinburgh, 2005, pp. 62–67
Zanker, M., Jessenitschnig, M.: Case-studies on exploiting explicit customer requirements in recommender systems. User Modeling and User-Adapted Interaction, vol. 19, pp 133–166. Springer, Netherlands (2009)
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The author is grateful to Dr. Brian Bloch for his comprehensive editing of the manuscript.
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De Carolis, B. (2011). Adapting News and Advertisements to Groups. In: Müller, J., Alt, F., Michelis, D. (eds) Pervasive Advertising. Human-Computer Interaction Series. Springer, London. https://doi.org/10.1007/978-0-85729-352-7_11
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