Abstract
Agent-based Web recommender systems are applications capable to generate useful suggestions for visitors of Web sites. This task is generally carried out by exploiting the interaction between two agents, one that supports the human user and the other that manages the Web site. However, in the case of large agent communities and in presence of a high number of Web sites these tasks are often too heavy for the agents, even more if they run on devices having limited resources. In order to address this issue, we propose a new multi-agent architecture, called MARS, where each user’s device is provided with a device agent, that autonomously collects information about the local user’s behaviour. A single profile agent, associated with the user, periodically collects such information coming from the different user’s devices to construct a global user profile. In order to generate recommendations, the recommender agent autonomously pre-computes data provided by the profile agents. This recommendation process is performed with the contribution of a site agent which indicates the recommendations to device agents that visit the Web site. This way, the site agent has the only task of suitably presenting the site content. We performed an experimental campaign on real data that shows the system works more effectively and more efficiently than other well-known agent-based recommenders.
This work has been partially supported by the MIUR–“Italian Ministry of Education, University and Research”, under the Research Project Quadrantis.
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References
Anderson, C.R., Domingos, P., Weld, D.S.: Adaptive web navigation for wireless devices. In: 17th Int. Joint Conf. on Artificial Intelligence, pp. 879–884 (2001)
Buccafurri, F., Lax, G., Rosaci, D., Ursino, D.: A user behavior-based agent for improving web usage. In: CoopIS/DOA/ODBASE, pp. 1168–1185 (2002)
Garruzzo, S., Modafferi, S., Rosaci, D., Ursino, D.: X-Compass: An XML Agent for Supporting User Navigation on the Web. In: Andreasen, T., Motro, A., Christiansen, H., Larsen, H.L. (eds.) FQAS 2002. LNCS (LNAI), vol. 2522, pp. 197–211. Springer, Heidelberg (2002)
Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: AAAI/IAAI, pp. 187–192 (2002)
Montaner, M., López, B., de la Rosa, J.L.: A taxonomy of recommender agents on the internet. Artif. Intell. Rev. vol. 19(4) (2003)
Parsons, J., Ralph, P., Gallagher, K.: Using viewing time to infer user preference in recommender systems. In: AAAI Workshop on Semantic Web Personalization, San Jose, USA, pp. 52–64 (July 2004)
Peñalvo, F.J.G., Paternò, F., Gil, A.B.: An adaptive e-commerce system definition. In: 2nd Int. Conf. on Adaptive Hypermedia and Adaptive Web-Based Systems
Rosaci, D., Sarné, G.M.L.: MASHA: A Multi Agent System Handling User and Device Adaptivity of Web Sites. User Modeling and User-Adapted Interaction: The Journal of Personalization Research, vol. 16(5)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: 2nd ACM Conference on Electronic Commerce (EC-00), Minneapolis, USA, October 2000, pp. 158–167. ACM Press, New York (2000)
Schafer, J.B., Konstan, J.A., Riedl, J.: E-commerce recommendation applications. Data Mining and Knowledge Discovery 5(1/2), 115–153 (2001)
Silvestri, F., Baraglia, R., Palmerini, P., Serranò, M.: On-line generation of suggestions for web users. J. of Digital Information Management 2(2), 104–108 (2004)
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Garruzzo, S., Rosaci, D., Sarné, G.M.L. (2007). MARS: An Agent-Based Recommender System for the Semantic Web. In: Indulska, J., Raymond, K. (eds) Distributed Applications and Interoperable Systems. DAIS 2007. Lecture Notes in Computer Science, vol 4531. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72883-2_14
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DOI: https://doi.org/10.1007/978-3-540-72883-2_14
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