Abstract
Case-Based Reasoning (CBR) is one of most successful applied AI technologies of recent years. Although many CBR systems reason locally on a previous experience base to solve new problems, in this paper we focus on distributed retrieval processes working on a network of collaborating CBR systems. In such systems, each node in a network of CBR agents collaborates, arguments and counterarguments its local results with other nodes to improve the performance of the system’s global response. We describe D2ISCO: a framework to design and implement deliberative and collaborative CBR systems that is integrated as a part of jcolibritwo an established framework in the CBR community. We apply D2ISCO to one particular simplified type of CBR systems: recommender systems. We perform a first case study for a collaborative music recommender system and present the results of an experiment of the accuracy of the system results using a fuzzy version of the argumentation system AMAL and a network topology based on a social network. Besides individual recommendation we also discuss how D2ISCO can be used to improve recommendations to groups and we present a second case of study based on the movie recommendation domain with heterogeneous groups according to the group personality composition and a group topology based on a social network.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Leake, D.B., et al.: Case-based reasoning: Experiences, lessons, and future directions. AAAI Press/MIT Press, Menlo Park (1996)
Plaza, E., McGinty, L.: Distributed case-based reasoning. Knowledge Eng. Review 20(3), 261–265 (2005)
McGinty, L., Smyth, B.: Collaborative case-based reasoning: Applications in personalised route planning. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 362–376. Springer, Heidelberg (2001)
Ontañón, S., Plaza, E.: Arguments and counterexamples in case-based joint deliberation. In: Maudet, N., Parsons, S., Rahwan, I. (eds.) ArgMAS 2006. LNCS (LNAI), vol. 4766, pp. 36–53. Springer, Heidelberg (2007)
Díaz-Agudo, B., González-Calero, P.A., Recio-García, J.A., Sánchez-Ruiz-Granados, A.A.: Building cbr systems with jcolibri. Sci. Comput. Program. 69(1-3), 68–75 (2007)
Díaz-Agudo, B., González-Calero, P.A., Recio-García, J.A., Sánchez, A.: Building cbr systems with jcolibri. Special Issue on Experimental Software and Toolkits of the Journal Science of Computer Programming 69(1-3), 68–75 (2007)
Bridge, D., Göker, M.H., McGinty, L., Smyth, B.: Case-based recommender systems. Knowledge Engineering Review 20(3), 315–320 (2006)
Grandison, T., Sloman, M.: A Survey of Trust in Internet Applications. IEEE Communications Surveys and Tutorials 3(4), 2–16 (2000)
Golbeck, J., Hendler, J.A.: Inferring binary trust relationships in web-based social networks. ACM Trans. Internet Techn. 6(4), 497–529 (2006)
McDonald, D.W.: Recommending collaboration with social networks: a comparative evaluation. In: CHI ’03: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 593–600. ACM, New York (2003)
Golbeck, J.: Generating predictive movie recommendations from trust in social networks. In: Stølen, K., Winsborough, W.H., Martinelli, F., Massacci, F. (eds.) iTrust 2006. LNCS, vol. 3986, pp. 93–104. Springer, Heidelberg (2006)
Ziegler, C.N., Golbeck, J.: Investigating interactions of trust and interest similarity. Decision Support Systems 43(2), 460–475 (2007)
Jameson, A., Smyth, B.: Recommendation to groups. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 596–627. Springer, Heidelberg (2007)
González-Sanz, S., Recio-García, J.A., Díaz-Agudo, B.: D\(^{\mbox{2}}\)ISCO: Distributed Deliberative CBR Systems with jCOLIBRI. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol. 5796, pp. 321–332. Springer, Heidelberg (2009)
Ontañón, S., Plaza, E.: An argumentation-based framework for deliberation in multi-agent systems. In: Rahwan, I., Parsons, S., Reed, C. (eds.) Argumentation in Multi-Agent Systems. LNCS (LNAI), vol. 4946, pp. 178–196. Springer, Heidelberg (2008)
Zimmermann, H.J.: Fuzzy set theory—and its applications, 3rd edn. Kluwer Academic Publishers, Norwell (1996)
Recio-García, J.A., Jimenez-Diaz, G., Sánchez-Ruiz-Granados, A.A., Díaz-Agudo, B.: Personality aware recommendations to groups. In: Proceedings of the 2009 ACM Conference on Recommender Systems, RecSys 2009, pp. 325–328. ACM, New York (2009)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)
Thomas, K., Kilmann, R.: Thomas-Kilmann Conflict Mode Instrument, Tuxedo, N.Y. (1974)
Masthoff, J., Gatt, A.: In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems. User Modeling and User-Adapted Interaction 16(3-4), 281–319 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Recio-García, J.A., Díaz-Agudo, B., González-Sanz, S., Sanchez, L.Q. (2010). Distributed Deliberative Recommender Systems. In: Nguyen, N.T., Kowalczyk, R. (eds) Transactions on Computational Collective Intelligence I. Lecture Notes in Computer Science, vol 6220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15034-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-15034-0_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15033-3
Online ISBN: 978-3-642-15034-0
eBook Packages: Computer ScienceComputer Science (R0)