Collaborative filtering is amongst the most preferred techniques when implementing recommender systems. Recently, great interest has turned towards parallel and distributed implementations of collaborative filtering algorithms. This work is a survey of the parallel and distributed collaborative filtering implementations, aiming not only to provide a comprehensive presentation of the field's development, but also to offer future research orientation by highlighting the issues that need to be further developed.
Context-aware technology enhanced recommender systems are discussed in one of the most recent surveys @cite_19 . A classification framework of the context information is introduced, which assigns the contextual information among 8 categories. The existing context-aware recommender systems that are used for technology enhanced learning are analysed concerning the proposed framework. Furthermore, the challenges encountered in the evolution of the field are commented.
@cite_19
2025605741
Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.