Multidimension Tensor Factorization Collaborative Filtering Recommendation

A recommended system (RS) seeks to predict the preference that a user would give to a product in use, provides personalized information for the identification of articles, generating suggestions that are beneficial and agile for the search of the required items or activities. The user can accept the recommendations by providing information that is stored in a database, and generates new suggestions. These systems are used in the most prominent platforms such as websites and social networks. These information filtering techniques focus on the main properties and characteristics of items and users. This paper presents an analysis of the recommended systems and the components involved in the development of their functions. It shows an individual approach to filtering techniques, classification of RSs, possible combinations of filtering techniques and finally the conclusions are obtained in the analysis of the Recommended Systems.

• Autores: Neira, Harold; García Guliany, Jesús; Cabás Vásquez, Luis
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2021
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/10889
• Acceso en línea: https://hdl.handle.net/11323/10889; https://repositorio.cuc.edu.co
• Palabra clave: Content-based; Filtering; Items; Multidimensional tensor; Recommendation
• Rights: closedAccess
• License: Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)