Array
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    [0] => stdClass Object
        (
            [journal] => stdClass Object
                (
                    [id_jnl] => 96
                )

        )

    [1] => stdClass Object
        (
            [section] => stdClass Object
                (
                    [section] => 1008
                )

        )

    [2] => stdClass Object
        (
            [title] => Array
                (
                    [0] => Generative Engine Optimization (GEO): a review of approaches, metrics, and strategies@en
                    [1] => Generative Engine Optimization (GEO): revisión de enfoques, métricas y estrategias@es
                )

        )

    [3] => stdClass Object
        (
            [abstract] => Array
                (
                    [0] => A state of the art is presented on the optimisation of content for generative search engines based on large language models (LLMs), in a context in which visibility no longer depends on traditional ranking but instead centres on the integration, influence and attribution of content within the responses generated by these systems. A literature review was carried out of recent scholarship on Generative Engine Optimisation (GEO) and other related approaches, analysing generative systems, evaluation metrics and optimisation strategies. The review shows a convergence around metrics of visibility, influence and verifiability, as well as around a recurring set of strategies that include improving content quality and structure, the use of structured data, the reinforcement of authority and entity-based optimisation. Optimisation for generative engines redefines the objectives of traditional SEO and raises new challenges in terms of attribution, stability of visibility and information quality. This work offers a framework of reference for future research and practical applications, especially those aimed at optimising content with a view to increasing visibility in response-generation environments produced by LLM-based systems@en
                    [1] => Se presenta un estado de la cuestión sobre la optimización de contenidos para motores de búsqueda generativos basados en grandes modelos de lenguaje (LLM), en un contexto en el que la visibilidad deja de depender del posicionamiento tradicional para centrarse en la integración, influencia y atribución del contenido en las respuestas generadas por estos sistemas. Se realizó una revisión bibliográfica de la literatura reciente sobre Generative Engine Optimization (GEO) y otros enfoques relacionados, analizando sistemas generativos, métricas de evaluación y estrategias de optimización. La revisión muestra una convergencia en torno a métricas de visibilidad, influencia y verificabilidad, así como a un conjunto recurrente de estrategias que incluyen la mejora de la calidad y estructura del contenido, el uso de datos estructurados, el refuerzo de la autoridad y la optimización basada en entidades. La optimización para motores generativos redefine los objetivos del SEO tradicional y plantea nuevos retos en términos de atribución, estabilidad de la visibilidad y calidad informativa. Este trabajo ofrece un marco de referencia para futuras investigaciones y aplicaciones prácticas, especialmente aquellas encaminadas a la optimización del contenido en aras de incrementar la visibilidad en entornos de generación de respuestas producidas por sistemas basados en LLMs.@es
                )

        )

    [4] => stdClass Object
        (
            [author] => Array
                (
                    [0] => Rubén Alcaraz-Martínez
                    [1] => Andreu Sulé
                )

        )

    [5] => stdClass Object
        (
            [subject] => Array
                (
                    [0] => Generative engine optimization (geo)@en
                    [1] => Generative search@en
                    [2] => Web visibility@en
                    [3] => Llm@en
                    [4] => Revisiones de la literatura@en
                    [5] => Generative engine optimization (geo)@es
                    [6] => Búsqueda generativa@es
                    [7] => Visibilidad web@es
                    [8] => Llm@es
                    [9] => Literature reviews@es
                )

        )

    [6] => stdClass Object
        (
            [source] => stdClass Object
                (
                    [vol] => 20
                    [nr] => 1
                    [year] => 2026
                    [theme] => 
                )

        )

    [7] => stdClass Object
        (
            [datePub] => Array
                (
                    [0] => 2026-06-23
                )

        )

    [8] => stdClass Object
        (
            [DOI] => Array
                (
                    [0] => stdClass Object
                        (
                            [type] => DOI
                            [value] => Array
                                (
                                    [0] => 10.54886/ibersid.v20i1.5156
                                )

                        )

                )

        )

    [9] => stdClass Object
        (
            [http] => Array
                (
                    [0] => stdClass Object
                        (
                            [type] => HTTP
                            [value] => Array
                                (
                                    [0] => https://www.ibersid.eu/ojs/index.php/ibersid/article/view/5156
                                )

                        )

                    [1] => stdClass Object
                        (
                            [type] => HTTP
                            [value] => Array
                                (
                                    [0] => https://www.ibersid.eu/ojs/index.php/ibersid/article/view/5156/4445
                                )

                        )

                )

        )

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

        )

    [11] => stdClass Object
        (
            [license] => Array
                (
                    [0] => RESERVED
                    [1] => by-nc-nd/4.0
                )

        )

)