User behavior and user experience analysis for social network services

Bao, Rong; Chen, Lei; Cui, Ping

doi:10.1007/s11276-019-02233-x

Cited by 11 publications

(3 citation statements)

References 24 publications

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“…In [8] he has used another method with supervision. According to the author, this method gives better results than the L-HMM method.…”

Section: Research Backgroundmentioning

confidence: 99%

Identify the Subject and Content of Tweets on Twitter Using Multilayer Neural Network Method and Random Graphs

Yazdanian,

Gerami,

Sadeghinia

2023

itrc

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The result of the research is a proposed model for text analysis and identifying the subject and content of texts on Twitter. In this model, two main phases are implemented for classification. In text mining problems and in text mining tasks in general, because the data used is unstructured text, there is a preprocessing phase to extract the feature from this unstructured data. Done. In the second phase of the proposed method, a multilayer neural network algorithm and random graphs are used to classify the texts. In fact, this algorithm is a method for classifying a text based on the training model. The results show a significant improvement. Comparing the proposed method with other methods, according to the results, we found that the proposed algorithm has a high percentage of improvement in accuracy and has a better performance than other methods. All the presented statistics and simulation output results of the proposed method are based on the implementation in MATLAB software.

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“…In [8] he has used another method with supervision. According to the author, this method gives better results than the L-HMM method.…”

Section: Research Backgroundmentioning

confidence: 99%

Identify the Subject and Content of Tweets on Twitter Using Multilayer Neural Network Method and Random Graphs

Yazdanian,

Gerami,

Sadeghinia

2023

itrc

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“…Using association rule technology, the minimum support [8] and minimum trust information of the users is extracted to derive the association rules between the two. According to the association rules, if the behavior trajectory of users is r j,T−l+1 , r j,T , • • • , r j,T−l , then it would mean that the behavior cluster of the user at that particular time is r j,T [9].…”

Section: Integration Of Museum User Behaviormentioning

confidence: 99%

Integration of Museum User Behavior Information Based on Wireless Network

Liu

Yang

Wang

2021

Mobile Information Systems

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Online museum information resource systems are getting popular these days which allow the users to get detailed information about the objects of their interest, and the user preferences are stored to search for related artifacts considering his/her online behavior. The behavior of users browsing online is integrated to capture relevant information which is integrated into museum information resources. Unfortunately, present implementations have errors in integration and optimization system, so a wireless network-based museum user behavior information integration system is proposed to calculate the user’s interest in museum’s cultural relics. The user behavior information resource model is developed based upon the degree of user interest, and forgetting functions with different decay rates are employed to describe changes in the interest level. This information is then used to construct users’ interest matrices. This matrix also contains information regarding the cultural relics that users have not yet visited. The system will introduce the interest weights of feature words to take the top features of the user behavior information for the integration of the users’ behavior and to combine the feature vectors that can represent the overall trajectory. Moreover, those feature vectors are described that can represent the local trajectory into feature vector to identify the slow-moving sparse targets, which is then utilized for the integration of users’ behavior information. The simulation tests prove that the proposed method can achieve low error in the integration process of user behavior information resources, thereby yielding good results.

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“…Figura 1. Descripción del problema Fuente: Elaboración propia a partir de otras fuentes (Barahona & Giulietti, 2017), (Relationship, 2011), (Bao et al, 2021), (Rojas López et al, 2009, (Ariel et al, 2016), (Walker, 2017).…”

Section: Introductionunclassified

Estrategia de monetización en el diseño de UX experiencia de usuario de las startups en plataformas web y aplicaciones móviles

Hernández Barajas

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El presente proyecto tiene como objetivo investigar y recopilar estrategias y prácticas de diseño de UX en plataformas web y aplicaciones móviles como propuesta de monetización y guía para las startups en Colombia. Con este propósito se realizó una revisión de literatura en artículos publicados sobre el tópico UX experiencia de usuario de la última década, integrando los aportes de los autores con un alto índice de citación disponibles en las bases de datos Web of Science - WOS, Scopus, Ebsco host, Springer link y Sciencedirect. La metodología se sustenta en una investigación de orden mixto para la elaboración de análisis de datos cuantitativos y cualitativos, inmerso en el desarrollo de las 5 etapas de la metodología del Design Thinking for Educators, considerada una de las metodologías innovadoras que utiliza la sensibilidad del diseñador y sus métodos de resolución de problemas para satisfacer las necesidades de las personas de un modo tecnológicamente factible y comercialmente viable Para tal efecto, se hace necesario contar con una mirada empresarial que permita integrar equipos de trabajo en las startups destacas en Colombia, identificando las principales tendencias del entorno, indicadores y estrategias de los procesos de diseño de UX, seleccionadas a partir de las variables de: Presencia en el mercado, historial empresarial, modelo de negocio: Delivery y crecimiento exponencial. Asimismo, estos indicadores se complementarán con la revisión de literatura del tópico UX con la finalidad de proponer un test diagnóstico como un producto mínimo viable como solución empresarial a las startups en función de la ideación, aplicación y desarrollo del diseño de UX en las plataformas web y aplicaciones móviles. Finalmente, los principales resultados se recopilan en 5 etapas de la metodología del Design Thinking for Educators: (Referencia) descubrimiento (Definición del desafío de diseño, revisión de literatura de UX, relevancia de las estrategias y prácticas de las startups en el mercado Colombiano) seguido de la etapa de interpretación (Diagnóstico cuantitativo y cualitativo), etapa de ideación (Proceso de generación de ideas) la etapa de experimentación (Construcción de la guía) y por último la etapa de evolución (donde se consolidan los principales aportes derivados del proceso de evaluación).

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User behavior and user experience analysis for social network services

Cited by 11 publications

References 24 publications

Identify the Subject and Content of Tweets on Twitter Using Multilayer Neural Network Method and Random Graphs

Identify the Subject and Content of Tweets on Twitter Using Multilayer Neural Network Method and Random Graphs

Integration of Museum User Behavior Information Based on Wireless Network

Estrategia de monetización en el diseño de UX experiencia de usuario de las startups en plataformas web y aplicaciones móviles

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