Guidelines for the Analysis and Design of Argumentation-Based Recommendation Systems

Abstract

Recommender systems study the characteristics of its users and applying different kinds of processing to the available data, find a subset of items that may be of interest to a given user in a specific situation. Argumentation-based tools offer the possibility of analyzing complex and dynamic domains by generating and analyzing arguments for and against recommending a specific item based on the users' preferences. This approach allows us to analyze the qualitative and quantitative characteristics of the recommended items, and to provide explanations to increase transparency. In this article, we develop a set of software engineering guidelines for the analysis and design of recommender systems leveraging this approach.