Skip to Main content Skip to Navigation
Conference papers

Experimenting Analogical Reasoning in Recommendation

Abstract : 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.
Keywords : Recommender systems
Complete list of metadatas

Cited literature [15 references]  Display  Hide  Download
Contributor : Open Archive Toulouse Archive Ouverte (oatao) <>
Submitted on : Wednesday, May 2, 2018 - 9:59:57 AM
Last modification on : Friday, June 12, 2020 - 3:52:35 AM
Long-term archiving on: : Tuesday, September 25, 2018 - 8:21:31 PM


Files produced by the author(s)


  • HAL Id : hal-01782596, version 1
  • OATAO : 18961


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⟩



Record views


Files downloads