An Approach based on Machine Learning Algorithms for the Recommendation of Scientific Cultural Heritage Objects

Abstract

The Scientific Cultural Heritage (SCH) of the Drâa-Tafilalet region in south-eastern Morocco is a rich source of data testifying to the ingenuity of an older generation that has shaped the past of the region. These data must be preserved for future generations, particularly with new technologies and the semantic web. Recommendation systems (RS) are intended to assist prospective users in recommending the most suitable services based on their profile and expectations. The collaborative filtering (CF), content filtering (CB) or hybrid filtering (CF) RS has shown promising results in order to explore the problems experienced especially in CH. However, there are some limitations to be resolved, mostly due to the ability of these methods to build a stable and complete framework, which can provide a complete image of the user profile and suggest the most appropriate offers. This paper presents a hybrid recommender system for SCH data; a field little explored despite its historical importance and the value it generates. The results presented in this paper belong to the data collected from the region of Drâa-Tafilalet in southern Morocco.

Road Accident Detection using SVM and Learning: A Comparative Study

“…In finance, machine learning methods examine large databases to identify fraudulent activity, enhance investment plans. Also recommendation systems depend heavily on machine learning, which has revolutionized the way people find relevant content and items on a variety of platforms [17], [18] and [19]. In road traffic management, machine learning has become crucial for optimizing traffic flow and enhancing safety through innovative applications like accident detection and prediction systems.…”