The development of automated recommender systems (RecSys) is a foreseeable phenomenon for contributing toward resolving the problem of information overload, valuing content and focusing attention on the user in such a context of overabundance. Qualitative evaluations of recommendations, the perspective of users and psychological factors are all perspectives of analysis which are specific to recommender systems and which open up new areas of research in this field with the help of abundant literature on techniques and algorithms. Collaborative filtering recommender systems are based on the statistical processing of opinions expressed by users. More broadly, the use of recommender systems is growing rapidly within the framework of e‐commerce. Online personalization proposes recommendations for products and services based on the online purchases of clients or their browsing habits. Personalization applications reduce information overload and provide services of added value
