An Argumentative Recommendation Approach Based on Contextual Aspects.

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 argumentationbased 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.