Abstract
Argumentation-based recommender systems constitute an interesting tool to provide reasoned recommendations in complex domains with unresolved contradictory information situations and incomplete information. In these systems, the use of contextual information becomes a central issue in order to come up with personalized recommendations. An argumentative recommender system that offers mechanisms to handle contextual aspects of the recommendation domain provides an important ability that can be exploited by the user. However, in most of existing works, this issue has not been extensively studied. In this work, we propose an argumentation-based formalization for dealing with this issue. We present a general framework that allows the design of recommender systems capable of handling queries that can include (possibly inconsistent) contextual information under which recommendations should be computed. To answer a query, in the proposed argumentation-based approach, the system first selects alternative instances according to the user’s supplied contextual information, and then makes recommendations, in both cases through a defeasible argumentative analysis.
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Acknowledgements
This work has been partially supported by EU H2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 690974 for the project MIREL: MIning and REasoning with Legal texts, and by funds provided by CONICET, Universidad Nacional del Sur by PGI-UNS (grant 24/N040), and Universidad Nacional de Entre Ríos. Godo acknowledges the Spanish FEDER/MINECO project TIN2015-71799- C2-1-P.
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Teze, J.C.L., Godo, L., Simari, G.R. (2018). An Argumentative Recommendation Approach Based on Contextual Aspects. In: Ciucci, D., Pasi, G., Vantaggi, B. (eds) Scalable Uncertainty Management. SUM 2018. Lecture Notes in Computer Science(), vol 11142. Springer, Cham. https://doi.org/10.1007/978-3-030-00461-3_31
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