A Content Based e-Commerce Recommendation Approach Under the Veristic Framework

  • Imen BenzartiEmail author
  • Hafedh Mili
  • Amandine Paillard
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 41)


A recommendation system is an automated tool that suggests an ordered list of appropriate items to a user. In this paper, we propose a recommendation algorithm that takes into account the variable and complex semantics of multi-valued properties, and the level of uncertainty or fuzziness inherent in the representations of users and items. In particular, we will rely on the concept of veristic variables, proposed by Yager, and propose algorithms for building user profiles, and for matching product descriptions to those profiles. We tested our algorithms on the Movielens datasets. Our results show statistically significant improvements in the f1-score and the Root Mean Square Error (RMSE) compared to baselines like Bayesian and K-nearest neighbors (KNN) approaches.


Recommendation algorithms Veristic variable Fuzzy sets Content based recommendation 


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Authors and Affiliations

  1. 1.LATECE LaboratoryUniversity of Quebec at MontrealMontrealCanada
  2. 2.University of MontpellierMontpellierFrance

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