Trabajo final de especialización: Predictive Accuracy of Machine Learning Algorithms in Recommender Systems
Fecha
2019
Autores
Título de la revista
ISSN de la revista
Título del volumen
Editor
Resumen
This work presents a systematic literature review on the application of Machine Learning
algorithms in the development of effective movie recommender systems. With the increasing popularity of movie recommender systems in the entertainment industry, selecting
appropriate algorithms has become crucial for delivering personalized and accurate recommendations to users. Through an extensive literature search and rigorous methodology,
this work identifies and analyzes commonly used Machine Learning algorithms for movie
recommendation. The accuracy and performance of these algorithms are evaluated using
established evaluation methods and metrics on movie datasets of different sizes. The
evaluation takes into account factors such as prediction accuracy, scalability, and robustness. The comparative analysis provides valuable insights into the effectiveness of various
Machine Learning algorithms in the context of movie recommendation. The findings
contribute to the understanding of algorithmic performance, enabling researchers and
practitioners to make informed decisions when developing movie recommender systems.
Additionally, the work explores the impact of different hyperparameters and optimization
techniques on algorithm performance. The results of this work aim to improve the quality
of movie recommendations and enhance user satisfaction. By providing guidelines and
recommendations for algorithm selection and optimization, this work contributes to the
advancement of movie recommender systems and the overall movie-watching experience.
Descripción
Palabras clave
APRENDIZAJE AUTOMATICO, ALGORITMOS, SISTEMAS DE RECOMENDACION, INTELIGENCIA ARTIFICIAL