Authors:
Jihene Latrech
;
Zahra Kodia
and
Nadia Ben Azzouna
Affiliation:
SMART-LAB, ISG Tunis, University of Tunis, Cite Bouchoucha, Bardo 2000, Tunis, Tunisia
Keyword(s):
Recommendation System, Collaborative Filtering, Clustering, Context-Driven, Contextual Similarity, Jensen-Shannon.
Abstract:
This research presents a machine learning-based context-driven collaborative filtering approach with three steps: contextual clustering, weighted similarity assessment, and collaborative filtering. User data is clustered across 3 aspects, and similarity scores are calculated, dynamically weighted, and aggregated into a normalized User-User similarity matrix. Collaborative filtering is then applied to generate contextual recommendations. Experiments on the LDOS-CoMoDa dataset demonstrated good performance, with RMSE and MAE rates of 0.5774 and 0.3333 respectively, outperforming reference approaches.