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Fundamentos de sistemas recomendadores

Denis Parra

El MOOC “Fundamentos de sistemas recomendadores” tiene como propósito que los estudiantes conozcan los principales conceptos asociados a estos sistemas, así como su evolución histórica. Se enseñarán las principales técnicas de recomendación, como son el filtrado colaborativo y el filtrado basado en contenido. Adicionalmente, se espera que los estudiantes conozcan y apliquen diversas métricas de evaluación que les permitan evaluar diferentes dimensiones de los sistemas de recomendación.

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What's inside

Syllabus

Bienvenida al curso
¡Bienvenidos y bienvenidas! Este curso tiene como propósito entregar los fundamentos de los sistemas de recomendación desde el punto de vista de entender el problema de recomendación personalizada, así como conocer y utilizar métodos y de métricas de evaluación.
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Diseñado para estudiantes curiosos por conocer los fundamentos de los sistemas de recomendación
Presenta conceptos y técnicas fundamentales para la comprensión y el desarrollo de sistemas de recomendación
Ofrece una exploración equilibrada de diversos enfoques, desde el filtrado colaborativo hasta la recomendación basada en contenido
Proporciona métricas de evaluación para medir la efectividad de los sistemas de recomendación
Requiere conocimientos previos en programación y álgebra lineal
El enfoque principal está en los conceptos y métodos, no en la implementación práctica

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Reviews summary

Fundamentos claros de sistemas recomendadores

Según los estudiantes, este curso ofrece una base muy sólida en sistemas de recomendación, ideal para principiantes y quienes buscan una comprensión teórica clara. Los alumnos destacan la claridad y concisión de las explicaciones del instructor, haciendo que conceptos complejos sean fáciles de entender. Si bien se elogia la estructura de los módulos y la cobertura de técnicas como el filtrado colaborativo, algunos comentan que el curso es más teórico que práctico. Existe una opinión recurrente de que, aunque proporciona las bases esenciales, se podría beneficiar de más ejemplos de código y aplicaciones prácticas, especialmente para quienes ya tienen alguna base o buscan profundizar en la implementación.
Excelente punto de partida para quienes se inician.
"Altamente recomendado para quienes empiezan en el campo de los sistemas de recomendación."
"Para mí, que soy principiante, fue perfecto y pude entender los conceptos sin dificultad."
"Lo recomendaría a cualquiera que esté empezando en el campo, ya que ofrece una base sólida."
Proporciona una base robusta y esencial en el campo.
"Este curso me dio una base muy sólida para entender cómo funcionan los sistemas de recomendación."
"El curso es excelente para entender los fundamentos de los sistemas de recomendación."
"Cubrió las expectativas que tenía para una introducción sólida y la parte de FunkSVD fue particularmente clara."
Las explicaciones del instructor son excepcionalmente claras.
"Este curso es excelente para entender los fundamentos... Los módulos están bien estructurados y los ejemplos son claros."
"Fantástico curso, muy claro y conciso. El profesor explica de maravilla y los conceptos se entienden fácilmente."
"Valoro mucho la claridad del profesor y la forma en que desglosa temas complejos, me ayudó a solidificar mi comprensión."
Puede necesitar estudio adicional para niveles avanzados.
"La profundidad en los modelos latentes podría ser mayor si buscas un nivel más avanzado."
"Siento que la información es un poco superficial si ya tienes alguna base y buscas especializarte."
"Podría beneficiarse de material complementario para avanzar a un nivel intermedio o de implementación."
Predomina la teoría, con menos énfasis en la práctica.
"Me hubiera gustado ver más ejemplos prácticos y código."
"No lo encontré muy útil. Poca práctica, mucho teoría."
"Siento que le faltaba profundidad en la parte de implementación. Es más teórico que práctico."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Fundamentos de sistemas recomendadores with these activities:
Revisit collaborative filtering
Review the basic concepts and techniques of collaborative filtering to strengthen your foundation.
Browse courses on Collaborative Filtering
Show steps
  • Examine different types of collaborative filtering
  • Analyze the process of user-based and item-based CF
  • Practice computing similarity measures
Work through recommendation system problems
Engage in targeted practice to improve your problem-solving skills and reinforce your understanding.
Show steps
  • Find practice problems or coding challenges
  • Solve these problems using different techniques
  • Analyze your solutions and identify areas for improvement
Explore advanced recommendation techniques
Seek out and follow tutorials to enhance your understanding of cutting-edge recommendation methods.
Browse courses on Matrix Factorization
Show steps
  • Identify specialized tutorials on specific techniques
  • Follow tutorials and implement the techniques
  • Experiment with different parameters and datasets
One other activity
Expand to see all activities and additional details
Show all four activities
Build a recommendation system prototype
Create a functional prototype of a recommendation system to apply and test your knowledge.
Browse courses on Recommendation Systems
Show steps
  • Design the architecture of your system
  • Choose and implement recommendation algorithms
  • Develop a user interface for your prototype
  • Test and iterate on your system's performance

Career center

Learners who complete Fundamentos de sistemas recomendadores will develop knowledge and skills that may be useful to these careers:
Software Developer
Software Developers may work on developing recommender systems. This course may be helpful for learning how recommender systems are built.
See salaries and explore the career path for Software Developer
Data Scientist
Data Scientists make use of recommender systems for tasks such as anomaly detection and fraud prevention. This course helps build a foundation for understanding how recommender systems work and how they can be implemented.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
Machine Learning Engineers will likely need to develop and implement recommender systems. This course can help build a foundaiton in recommender systems, which will be helpful for working with them.
See salaries and explore the career path for Machine Learning Engineer
Quantitative Analyst
Quantitative Analysts will likely make use of recommender systems in their work. This course may be helpful for building a foundation in recommender systems.
See salaries and explore the career path for Quantitative Analyst
Statistician
Statisticians will likely make use of recommender systems for tasks such as fraud detection. This course may be helpful for building a foundation on how recommender systems work and how they can be implemented.
See salaries and explore the career path for Statistician
Product Manager
Product Managers make decisions on new features and products, which requires understanding user needs. Recommender systems can help with understanding user needs. This course helps build a foundation for understanding recommender systems and how they help users.
See salaries and explore the career path for Product Manager
Data Analyst
A Data Analyst may make use of recommender systems to extract insights from data, a process that may then be used to inform decisions on marketing or product development. This course helps build a foundation for understanding what recommender systems are and how they are built.
See salaries and explore the career path for Data Analyst
User Experience Researcher
User Experience Researchers will want to understand recommender systems to make informed recommendations on improving user experiences. This course can help build a foundation for engaging with more technical stakeholders working on recommender systems.
See salaries and explore the career path for User Experience Researcher
Business Analyst
Business Analysts will likely make use of data from recommender systems to inform business decisions. This course may be helpful for building a foundation in recommender systems.
See salaries and explore the career path for Business Analyst
Marketing Manager
Marketing Managers may work closely with teams developing and implementing recomender systems. This course may be helpful for building a foundation in recommender systems and how they work with marketing initiatives.
See salaries and explore the career path for Marketing Manager
Sales Manager
Sales Managers may work closely with teams developing and implementing recomender systems. This course may be helpful for building a foundation in recommender systems and how they work with sales initiatives.
See salaries and explore the career path for Sales Manager
Technical Writer
Technical Writers may be responsible for generating user documentation for recommender systems. This course may be helpful for learning the fundamentals of how recommender systems work.
See salaries and explore the career path for Technical Writer
Customer Success Manager
Customer Success Managers may make use of data from recommender systems to identify and address customer needs. This course may be helpful for understanding what recommender systems are and how they can inform marketing decisions.
See salaries and explore the career path for Customer Success Manager
Quality Assurance Analyst
Quality Assurance Analysts may be responsible for testing recommender systems. This course may be useful for building a foundation in recommender systems and how they are implemented.
See salaries and explore the career path for Quality Assurance Analyst
Information Security Analyst
Information Security Analysts may work on projects securing recommender systems. This course may be useful for learning how recommender systems work.
See salaries and explore the career path for Information Security Analyst

Reading list

We've selected five books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Fundamentos de sistemas recomendadores.
Cover image
Neural Networks and Deep Learning
Save
Este libro de texto integral proporciona una base sólida en los conceptos y algoritmos subyacentes a los sistemas de recomendación, incluyendo técnicas avanzadas como el deep learning.
(English) Neural Networks and Deep Learning: A Textbook
Hardcover
$$
(English) Neural Networks and Deep Learning: A Textbook
Kindle Edition
$$
(中文) Neural network and deep learning(Chinese Edition)
Paperback
$$
Cover image
Recommender Systems
Save
Este es un libro completo que cubre todos los aspectos de los sistemas de recomendación. Es un recurso valioso para cualquiera que quiera aprender más sobre este campo.
Recommender Systems
Paperback
Check
Recommender Systems
Kindle Edition
Check
Cover image
Disaster Robotics
Save
Provides a comprehensive overview of recommender systems for news. It discusses the unique challenges and opportunities of building recommender systems in this context, and presents a variety of techniques for doing so.
(English) Disaster Robotics (Intelligent Robotics and...
Paperback
$$
(English) Adoption of Robots for Disasters: Lessons from the...
Paperback
$$$
(English) Disaster Robotics (Intelligent Robotics and...
Kindle Edition
$$
Recommender Systems
Save
Libro de texto escrito por expertos, que proporciona una introducción completa a los sistemas de recomendación, con un enfoque en algoritmos y aplicaciones prácticas.
Recommender Systems
Paperback
Check
Recommender Systems
Kindle Edition
Check
Cover image
Neural Networks and Deep Learning
Save
Este libro se centra en las aplicaciones de los sistemas de recomendación en el comercio electrónico. Proporciona una descripción general de los diferentes tipos de sistemas de recomendación utilizados en el comercio electrónico y su rendimiento.
(English) Neural Networks and Deep Learning: A Textbook
Hardcover
$$
(English) Neural Networks and Deep Learning: A Textbook
Hardcover
$$$
(中文) Neural network and deep learning(Chinese Edition)
Paperback
$$
(English) Neural Networks and Deep Learning: A Textbook
Kindle Edition
$$

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Course image
Effort
4 horas/semana
Level
Intermediate
Via
Coursera
Institution
Pontificia Universidad Católica de Chile
Instructor
Denis Parra
Language
Español
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Diseñado para estudiantes curiosos por conocer los fundamentos de los sistemas de recomendación
Presenta conceptos y técnicas fundamentales para la comprensión y el desarrollo de sistemas de recomendación
Ofrece una exploración equilibrada de diversos enfoques, desde el filtrado colaborativo hasta la recomendación basada en contenido
Proporciona métricas de evaluación para medir la efectividad de los sistemas de recomendación
Requiere conocimientos previos en programación y álgebra lineal
El enfoque principal está en los conceptos y métodos, no en la implementación práctica
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