Predictive Analysis and Data Visualization Approach for Decision Processes in Marketing Strategies: A Case of Study
In this paper, we perform a new strategy for recommender systems in online entertainment platforms. As a case of study, we analyzed the reading preferences based on users of Goodreads, a social network for readers, to classify the books depending on their associated with variables as average rating,...
- Autores:
-
García-Pérez, Andrés
Millán Hernández, María Alejandra
Castellón Marriaga, Daniela E.
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9551
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9551
https://link.springer.com/chapter/10.1007/978-3-030-61834-6_6
- Palabra clave:
- Machine learning
Predictive analytics
Data visualization
Recommender systems
Marketing strategies
- Rights
- closedAccess
- License
- http://purl.org/coar/access_right/c_14cb
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Predictive Analysis and Data Visualization Approach for Decision Processes in Marketing Strategies: A Case of Study |
| title |
Predictive Analysis and Data Visualization Approach for Decision Processes in Marketing Strategies: A Case of Study |
| spellingShingle |
Predictive Analysis and Data Visualization Approach for Decision Processes in Marketing Strategies: A Case of Study Machine learning Predictive analytics Data visualization Recommender systems Marketing strategies |
| title_short |
Predictive Analysis and Data Visualization Approach for Decision Processes in Marketing Strategies: A Case of Study |
| title_full |
Predictive Analysis and Data Visualization Approach for Decision Processes in Marketing Strategies: A Case of Study |
| title_fullStr |
Predictive Analysis and Data Visualization Approach for Decision Processes in Marketing Strategies: A Case of Study |
| title_full_unstemmed |
Predictive Analysis and Data Visualization Approach for Decision Processes in Marketing Strategies: A Case of Study |
| title_sort |
Predictive Analysis and Data Visualization Approach for Decision Processes in Marketing Strategies: A Case of Study |
| dc.creator.fl_str_mv |
García-Pérez, Andrés Millán Hernández, María Alejandra Castellón Marriaga, Daniela E. |
| dc.contributor.author.none.fl_str_mv |
García-Pérez, Andrés Millán Hernández, María Alejandra Castellón Marriaga, Daniela E. |
| dc.subject.keywords.spa.fl_str_mv |
Machine learning Predictive analytics Data visualization Recommender systems Marketing strategies |
| topic |
Machine learning Predictive analytics Data visualization Recommender systems Marketing strategies |
| description |
In this paper, we perform a new strategy for recommender systems in online entertainment platforms. As a case of study, we analyzed the reading preferences based on users of Goodreads, a social network for readers, to classify the books depending on their associated with variables as average rating, rating count, and text review count. Multivariate techniques cluster analysis and benchmarking for comparison of predictive models were used. Graphs and data are presented, allowing optimal evaluation of the number of clusters and the precision of models. Finally, we show the existence of groups of elements that can be forgotten by traditional recommendation systems, due to their low visualization on the platform. It is proposed to use promotional strategies to highlight these high-quality articles but with little visibility. All in all, consider the classification of books that predictive models can offer, it can favor the authors, readers, and investors of Goodreads, by the retention and attraction of users. |
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2020 |
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2020-11-04T21:46:31Z |
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2020-11-04T21:46:31Z |
| dc.date.issued.none.fl_str_mv |
2020-10-08 |
| dc.date.submitted.none.fl_str_mv |
2020-11-04 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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García-Pérez A., Hernández M.A.M., Castellón Marriaga D.E. (2020) Predictive Analysis and Data Visualization Approach for Decision Processes in Marketing Strategies: A Case of Study. In: Figueroa-García J.C., Garay-Rairán F.S., Hernández-Pérez G.J., Díaz-Gutierrez Y. (eds) Applied Computer Sciences in Engineering. WEA 2020. Communications in Computer and Information Science, vol 1274. Springer, Cham. https://doi.org/10.1007/978-3-030-61834-6_6 |
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https://hdl.handle.net/20.500.12585/9551 |
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https://link.springer.com/chapter/10.1007/978-3-030-61834-6_6 |
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10.1007/978-3-030-61834-6_6 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
García-Pérez A., Hernández M.A.M., Castellón Marriaga D.E. (2020) Predictive Analysis and Data Visualization Approach for Decision Processes in Marketing Strategies: A Case of Study. In: Figueroa-García J.C., Garay-Rairán F.S., Hernández-Pérez G.J., Díaz-Gutierrez Y. (eds) Applied Computer Sciences in Engineering. WEA 2020. Communications in Computer and Information Science, vol 1274. Springer, Cham. https://doi.org/10.1007/978-3-030-61834-6_6 10.1007/978-3-030-61834-6_6 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/9551 https://link.springer.com/chapter/10.1007/978-3-030-61834-6_6 |
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eng |
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eng |
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García-Pérez, Andrésee84267c-8287-476e-bcae-7f10d7f1a8a5Millán Hernández, María Alejandra0349cdb4-74a1-4060-83bb-e6d84ff439a6Castellón Marriaga, Daniela E.07f11df5-8554-496b-b95d-e45fc550d97d2020-11-04T21:46:31Z2020-11-04T21:46:31Z2020-10-082020-11-04García-Pérez A., Hernández M.A.M., Castellón Marriaga D.E. (2020) Predictive Analysis and Data Visualization Approach for Decision Processes in Marketing Strategies: A Case of Study. In: Figueroa-García J.C., Garay-Rairán F.S., Hernández-Pérez G.J., Díaz-Gutierrez Y. (eds) Applied Computer Sciences in Engineering. WEA 2020. Communications in Computer and Information Science, vol 1274. Springer, Cham. https://doi.org/10.1007/978-3-030-61834-6_6https://hdl.handle.net/20.500.12585/9551https://link.springer.com/chapter/10.1007/978-3-030-61834-6_610.1007/978-3-030-61834-6_6Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarIn this paper, we perform a new strategy for recommender systems in online entertainment platforms. As a case of study, we analyzed the reading preferences based on users of Goodreads, a social network for readers, to classify the books depending on their associated with variables as average rating, rating count, and text review count. Multivariate techniques cluster analysis and benchmarking for comparison of predictive models were used. Graphs and data are presented, allowing optimal evaluation of the number of clusters and the precision of models. Finally, we show the existence of groups of elements that can be forgotten by traditional recommendation systems, due to their low visualization on the platform. It is proposed to use promotional strategies to highlight these high-quality articles but with little visibility. All in all, consider the classification of books that predictive models can offer, it can favor the authors, readers, and investors of Goodreads, by the retention and attraction of users.application/pdfengApplied Computer Sciences in Engineering. WEA 2020. Communications in Computer and Information Science, vol 1274Predictive Analysis and Data Visualization Approach for Decision Processes in Marketing Strategies: A Case of Studyinfo:eu-repo/semantics/lectureinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_8544Machine learningPredictive analyticsData visualizationRecommender systemsMarketing strategiesinfo:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbCartagena de IndiasPúblico generalShatzkin, M., Riger, R .: The Book Business, 1ª ed. Oxford University Press, Nueva York (2019)Rana, A., Deeba, K .: Sistema de recomendación de libros en línea que usa filtrado colaborativo (con similitud Jaccard). Nano Sci. J. Phys. Conf. Ser. 1362 , 12130 (2019). https://doi.org/10.1088/1742-6596/1362/1/012130Adomavicius, G., Tuzhilin, A .: Hacia la próxima generación de sistemas de recomendación: un estudio del estado de la técnica y posibles ampliaciones. IEEE Trans. Knowl. Ing. De datos 17 (6), 734–749 (2005). https://doi.org/10.1109/TKDE.2005.99Resnick, P., Iakovou, N .: GroupLens: una arquitectura abierta para el filtrado colaborativo de netnews. En: Conferencia sobre trabajo cooperativo asistido por computadora (1994)Hill, W., Stead, L., Rosenstein, M., Furnas, G .: Recomendar y evaluar opciones en una comunidad virtual de uso. En: Actas de la Conferencia sobre factores humanos en sistemas informáticos (1995)Sarwar, B., Karypis, G., Konstan, J .: Algoritmos de recomendación de filtrado colaborativo basados en elementos. En: Actas de la X Conferencia Internacional WWW (2001)Lang, K .: Newsweeder: aprender a filtrar las noticias de la red. En: Actas de la 12a Conferencia Internacional de Aprendizaje Automático (1995)Balabanovic, M., Shoham, Y .: Fab: recomendación colaborativa basada en contenido. Comm. ACM 40 (3), 66–72 (1997)Pazzani, M., Billsus, D .: Aprender y revisar los perfiles de usuario: la identificación de sitios web interesantes. Mach. Aprender. 27 , 313–331 (1997)Claypool, M., Gokhale, A., Miranda, T .: Combinando filtros basados en contenido y colaborativos en un periódico en línea. En: Actas de ACM SIGIR 1999 Workshop RecomendadorTran, T, Cohen., R .: Sistemas de recomendación híbridos para comercio electrónico. En: Proceedings of Knowledge-Based Electronic Markets. Artículos del Taller AAAI, Informe técnico WS-00-04, AAAI Press (2000)Melville, P., Mooney, R .: filtrado colaborativo impulsado por contenido para recomendaciones mejoradas. En: Actas de la 18a Conferencia Nacional de Inteligencia Artificial (2002)Liu, Q., Chen, E., Xiong, H., Ding, CHQ, Chen, J .: Mejora del filtrado colaborativo mediante la expansión del interés del usuario mediante clasificación personalizada. IEEE Trans. Syst. Hombre Cybern. Parte B Cybern. 42 (1), 2012 (2012)Strub, F., Gaudel, R., Mary, J .: Sistema de recomendación híbrido basado en codificadores automáticos. En: Actas del primer taller sobre aprendizaje profundo para sistemas de recomendación, ACM, págs. 11-16 (2016)Zhang, S., Yao, L., Sun, A .: Sistema de recomendación basado en aprendizaje profundo: una encuesta y nuevas perspectivas. preimpresión de arXiv arXiv: 1707.07435 (2017)Feng, J., Fengs, X., Zhang, N., Peng, J .: Un método de filtrado colaborativo mejorado basado en la similitud. PLoS ONE 13 (9), e0204003 (2018)Kaggle (2020). https://www.kaggle.com/jealousleopard/goodreadsbooksBholowalia, P., Kumar, A .: EBK-means: una técnica de agrupamiento basada en el método del codo y k-means en WSN. En t. J. Comput. Apl. 105 (9), 17 a 24 (2014)Rousseeuw, P .: Siluetas: una ayuda gráfica para la interpretación y validación del análisis de conglomerados. J. Comput. Apl. Matemáticas. 20 , 53–65 (1987). https://doi.org/10.1016/0377-0427(87)90125-7 . 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