Experimenting Analogical Reasoning in Recommendation

Nicolas Hug (1) , Henri Prade (2, 3) , Gilles Richard (2)

1 IRIT - Institut de recherche en informatique de Toulouse
2 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage
3 CNRS - Centre National de la Recherche Scientifique

Recommender systems aim at providing suggestions of interest for end-users. Two main types of approach underlie existing recommender systems: content-based methods and collaborative filtering. In this paper, encouraged by good results obtained in classification by analogical proportion-based techniques, we investigate the possibility of using analogy as the main underlying principle for implementing a prediction algorithm of the collaborative filtering type. The quality of a recommender system can be estimated along diverse dimensions. The accuracy to predict user’s rating for unseen items is clearly an important matter. Still other dimensions like coverage and surprise are also of great interest. In this paper, we describe our implementation and we compare the proposed approach with well-known recommender systems.

Mots clés: Recommender systems

Domaines: Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Logique en informatique [cs.LO]; Informatique et langage [cs.CL]

Nicolas Hug, Henri Prade, Gilles Richard. Experimenting Analogical Reasoning in Recommendation. 22nd International Symposium on Methodologies for Intelligent Systems (ISMIS 2015), Oct 2015, Lyon, France. pp. 69-78. ⟨hal-01782596⟩