2013/03/10 by Djallel Bouneffouf, Bouneffouf, Djallel
Computer Science · #Advanced Image and Video Retrieval Techniques #FOS: Computer and information sciences #I.2 #Image Retrieval and Classification Techniques #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Recommender Systems and Techniques #cs.IR #cs.LG
paper · pdf · doi:10.48550/arxiv.1303.2651
arXiv admin note: substantial text overlap with arXiv:1301.4351, arXiv:1303.2308
openalex publication_date 2013/03/10 · arxiv created 2014/03/30 · arxiv updated 2014/04/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Ubiquitous information access becomes more and more important nowadays and research is aimed at making it adapted to users. Our work consists in applying machine learning techniques in order to bring a solution to some of the problems concerning the acceptance of the system by users. To achieve this, we propose a fundamental shift in terms of how we model the learning of recommender system: inspired by models of human reasoning developed in robotic, we combine reinforcement learning and case-base reasoning to define a recommendation process that uses these two approaches for generating recommendations on different context dimensions (social, temporal, geographic). We describe an implementation of the recommender system based on this framework. We also present preliminary results from experiments with the system and show how our approach increases the recommendation quality.