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

LEIVA, MARIO; BUDAN, MAXIMILIANO C. D.; SIMARI, GERARDO I.

IEEE INTELLIGENT SYSTEMS

IEEE COMPUTER SOC

Año: 2020 vol. 35 p. 28 - 37

1541-1672

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 studying arguments for and against recommending a specific item based on the users´ preferences. This approach allows to analyze the qualitative and quantitative characteristics of the recommended items, as well as to provide explanations about the recommendations, increasing the transparency of the process. In this work, we develop a set of guidelines for the analysis and design of recommender systems leveraging this approach.