Array
(
    [0] => stdClass Object
        (
            [journal] => stdClass Object
                (
                    [id_jnl] => 119
                )

        )

    [1] => stdClass Object
        (
            [section] => stdClass Object
                (
                    [section] => 2173
                )

        )

    [2] => stdClass Object
        (
            [title] => Array
                (
                    [0] => A hybrid book recommendation model using deep learning, collaborative, and content filtering@en
                    [1] => Un modelo híbrido para la recomendación de libros utilizando reconocimiento facial, filtrado colaborativo y por contenido@es
                )

        )

    [3] => stdClass Object
        (
            [abstract] => Array
                (
                    [0] => The continuous evolution of technology is transforming the way libraries interact with their users, and in turn, how users engage with books. Recommendation systems are conceived as information filtering systems whose goal is to provide access to personalized information (books of interest, magazines, databases, scientific articles, rooms, etc.) to enhance the user experience, promote the usability of bibliographic resources, and optimize services. This article proposes a hybrid model for the automatic recommendation of books that combines three processes in two phases: user identification, collaborative filtering, and content filtering. In the first phase, the user recognition process is carried out using techniques that implement deep learning, and in the second phase, the recommendation processes are integrated through collaborative filtering and content filtering. A case study was developed in a library environment for recommending books and was evaluated using classic information retrieval metrics. The results were compared with other more robust recommendation models, obtaining satisfactory outcomes.@en
                    [1] => La continua evolución de la tecnología transforma la manera en que las bibliotecas interactúan con sus usuarios, y estos a su vez con los libros. Los sistemas de recomendación se conciben como sistemas de filtrado de información cuyo objetivo es proporcionar acceso a información personalizada (libros de interés, revistas, bases de datos, artículos científicos, salas, etc.) para mejorar la experiencia del usuario, fomentar la usabilidad de los recursos bibliográficos y optimizar los servicios. Este artículo propone un modelo híbrido de recomendación automática de libros que ensambla tres procesos en dos fases: identificación del usuario, filtrado colaborativo y filtrado por contenido. En la primera fase, se lleva a cabo el proceso de reconocimiento de usuario con técnicas que implementan aprendizaje profundo y, en la segunda fase, se integran los procesos de recomendación mediante filtrado colaborativo y por contenido. Se elaboró un caso de estudio en un entorno biblioecario para recomendar libros y fue evaluado mediante métricas clásicas de recuperación de información. Se compararon los resultados con otros modelos de recomendación más robustos, obteniendo resultados satisfactorios.@es
                )

        )

    [4] => stdClass Object
        (
            [author] => Array
                (
                    [0] => Eduard Gilberto Puerto Cuadros
                )

        )

    [5] => stdClass Object
        (
            [subject] => Array
                (
                    [0] => Recommendation system@en
                    [1] => Facial recognition@en
                    [2] => Deep learning@en
                    [3] => Collaborative filtering@en
                    [4] => Content filtering@en
                    [5] => Library@en
                    [6] => Sistema de recomendación@es
                    [7] => Reconocimiento facial@es
                    [8] => Aprendizaje profundo@es
                    [9] => Filtrado colaborativo@es
                    [10] => Filtrado por contenido@es
                    [11] => Biblioteca@es
                )

        )

    [6] => stdClass Object
        (
            [source] => stdClass Object
                (
                    [vol] => 34
                    [nr] => 1
                    [year] => 2024
                    [theme] => 
                )

        )

    [7] => stdClass Object
        (
            [datePub] => Array
                (
                    [0] => 2024-07-18
                )

        )

    [8] => stdClass Object
        (
            [DOI] => Array
                (
                    [0] => stdClass Object
                        (
                            [type] => DOI
                            [value] => Array
                                (
                                    [0] => 10.5209/rgid.94234
                                )

                        )

                )

        )

    [9] => stdClass Object
        (
            [http] => Array
                (
                    [0] => stdClass Object
                        (
                            [type] => HTTP
                            [value] => Array
                                (
                                    [0] => https://revistas.ucm.es/index.php/RGID/article/view/94234
                                )

                        )

                    [1] => stdClass Object
                        (
                            [type] => HTTP
                            [value] => Array
                                (
                                    [0] => https://revistas.ucm.es/index.php/RGID/article/view/94234/4564456570183
                                )

                        )

                    [2] => stdClass Object
                        (
                            [type] => HTTP
                            [value] => Array
                                (
                                    [0] => https://github.com/ageitgey/face_recognition
                                    [1] => https://ieeexplore.ieee.org/document/9894475
                                )

                        )

                )

        )

    [10] => stdClass Object
        (
            [language] => Array
                (
                    [0] => es
                )

        )

    [11] => stdClass Object
        (
            [license] => Array
                (
                    [0] => stdClass Object
                        (
                            [@xml:lang] => es-ES
                            [#text] => Derechos de autor 2024 Revista General de Información y Documentación
                        )

                )

        )

)