A Categorical Transformer with a Data Science Approach for Recommendation Systems Based on Collaborative Filtering

Remigio Hurtado; Arantxa Muñoz

doi:10.1007/978-981-97-3556-3_21

A Categorical Transformer with a Data Science Approach for Recommendation Systems Based on Collaborative Filtering

Remigio Hurtado
, Arantxa Muñoz

Grupo de Investigación de Inteligencia Artificial y Tecnologías de Asistencia (GI-IATA)

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

Resumen

Recommender systems help predict what customers might like, such as movies, restaurants, or products. Collaborative filtering, a crucial part of these systems, faces challenges when dealing with user, item, and rating data. Traditional machine learning struggles with this data because user and item data are categorical. To solve this, we propose a method that transforms the original data into new variables, making it more suitable for advanced machine learning and deep learning techniques. This approach enhances prediction quality and opens doors for innovative data processing methods in collaborative filtering.

Idioma original	Inglés
Título de la publicación alojada	Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024
Editores	Xin-She Yang, Simon Sherratt, Nilanjan Dey, Amit Joshi
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	261-271
Número de páginas	11
ISBN (versión impresa)	9789819735556
DOI	https://doi.org/10.1007/978-981-97-3556-3_21
Estado	Publicada - 2024
Evento	9th International Congress on Information and Communication Technology, ICICT 2024 - London, Reino Unido Duración: 19 feb 2024 → 22 feb 2024

Serie de la publicación

Nombre	Lecture Notes in Networks and Systems
Volumen	1012 LNNS
ISSN (versión impresa)	2367-3370
ISSN (versión digital)	2367-3389

Conferencia

Conferencia	9th International Congress on Information and Communication Technology, ICICT 2024
País/Territorio	Reino Unido
Ciudad	London
Período	19/02/24 → 22/02/24

Nota bibliográfica

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Areas de Conocimiento del CACES

245A Estadísticas
8116A Sistemas de Información

Acceder al documento

10.1007/978-981-97-3556-3_21

Otros archivos y enlaces

Enlace a la publicación en Scopus

AI-EduResearch: Plataforma de Apoyo a la Investigación y el Aprendizaje Potenciada por Modelos de Inteligencia Artificial y Machine Learning
Bojorque Chasi, R. X. (Investigador Secundario), Hurtado Ortiz, R. I. (Investigador principal), Lopez Arizaga, A. B. (Estudiante Investigador), Dutan Sanchez, D. G. (Estudiante Investigador), Alvarado Orellana, D. F. (Estudiante Investigador), Malo Vega, J. J. (Estudiante Investigador), Amendaño Quizhpi, E. P. (Estudiante Investigador) & Tacuri Delgado, H. S. (Estudiante Investigador)
18/01/24 → 4/07/25
Proyecto: Investigación y Desarrollo

Citar esto

Hurtado, R., & Muñoz, A. (2024). A Categorical Transformer with a Data Science Approach for Recommendation Systems Based on Collaborative Filtering. En X.-S. Yang, S. Sherratt, N. Dey, & A. Joshi (Eds.), Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024 (pp. 261-271). (Lecture Notes in Networks and Systems; Vol. 1012 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-97-3556-3_21

Hurtado, Remigio ; Muñoz, Arantxa. / A Categorical Transformer with a Data Science Approach for Recommendation Systems Based on Collaborative Filtering. Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024. editor / Xin-She Yang ; Simon Sherratt ; Nilanjan Dey ; Amit Joshi. Springer Science and Business Media Deutschland GmbH, 2024. pp. 261-271 (Lecture Notes in Networks and Systems).

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title = "A Categorical Transformer with a Data Science Approach for Recommendation Systems Based on Collaborative Filtering",

abstract = "Recommender systems help predict what customers might like, such as movies, restaurants, or products. Collaborative filtering, a crucial part of these systems, faces challenges when dealing with user, item, and rating data. Traditional machine learning struggles with this data because user and item data are categorical. To solve this, we propose a method that transforms the original data into new variables, making it more suitable for advanced machine learning and deep learning techniques. This approach enhances prediction quality and opens doors for innovative data processing methods in collaborative filtering.",

keywords = "Categorical transformer, Collaborative filtering, Data science, Data transformation, Machine learning, Recommender systems",

author = "Remigio Hurtado and Arantxa Mu{\~n}oz",

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Hurtado, R & Muñoz, A 2024, A Categorical Transformer with a Data Science Approach for Recommendation Systems Based on Collaborative Filtering. En X-S Yang, S Sherratt, N Dey & A Joshi (eds.), Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024. Lecture Notes in Networks and Systems, vol. 1012 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 261-271, 9th International Congress on Information and Communication Technology, ICICT 2024, London, Reino Unido, 19/02/24. https://doi.org/10.1007/978-981-97-3556-3_21

A Categorical Transformer with a Data Science Approach for Recommendation Systems Based on Collaborative Filtering. / Hurtado, Remigio; Muñoz, Arantxa.
Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024. ed. / Xin-She Yang; Simon Sherratt; Nilanjan Dey; Amit Joshi. Springer Science and Business Media Deutschland GmbH, 2024. p. 261-271 (Lecture Notes in Networks and Systems; Vol. 1012 LNNS).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

TY - GEN

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PY - 2024

Y1 - 2024

N2 - Recommender systems help predict what customers might like, such as movies, restaurants, or products. Collaborative filtering, a crucial part of these systems, faces challenges when dealing with user, item, and rating data. Traditional machine learning struggles with this data because user and item data are categorical. To solve this, we propose a method that transforms the original data into new variables, making it more suitable for advanced machine learning and deep learning techniques. This approach enhances prediction quality and opens doors for innovative data processing methods in collaborative filtering.

AB - Recommender systems help predict what customers might like, such as movies, restaurants, or products. Collaborative filtering, a crucial part of these systems, faces challenges when dealing with user, item, and rating data. Traditional machine learning struggles with this data because user and item data are categorical. To solve this, we propose a method that transforms the original data into new variables, making it more suitable for advanced machine learning and deep learning techniques. This approach enhances prediction quality and opens doors for innovative data processing methods in collaborative filtering.

KW - Categorical transformer

KW - Collaborative filtering

KW - Data science

KW - Data transformation

KW - Machine learning

KW - Recommender systems

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BT - Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024

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Hurtado R, Muñoz A. A Categorical Transformer with a Data Science Approach for Recommendation Systems Based on Collaborative Filtering. En Yang XS, Sherratt S, Dey N, Joshi A, editores, Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024. Springer Science and Business Media Deutschland GmbH. 2024. p. 261-271. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-97-3556-3_21