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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.
Register for this course and see more details by visiting:
OpenCourser.com/course/8g4ygw/fundamentos
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
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
Módulo 1: Conociendo los sistemas de recomendación
En este módulo aprenderás qué es un sistema de recomendación a partir de definiciones y ejemplos, veremos diferentes tipos de sistemas de recomendación e introduciremos cómo hacer recomendación no personalizada.
Módulo 2: Filtrado Colaborativo
En este módulo aprenderás qué es el filtrado colaborativa, diferentes versiones como la basada en usuarios y la basada en ítems, así como la técnica pendiente uno.
Módulo 3: Recomendación basada en contenido y evaluación vía ranking
En este módulo aprenderás sobre la recomendación basada en contenido, cómo usar descripciones textuales para recomendar y cómo evaluar un sistema de recomendación usando métricas de ranking.
Módulo 4: Métodos latentes de factorización matricial y FunkSVD
En este módulo aprenderás sobre métodos latentes, su relación con factorización matricial y cómo usarla para predecir ratings y hacer recomendaciones.
Cierre del curso
Les queremos agradecer el habernos acompañado en el curso. Esperamos que los contenidos abordados sean un real aporte en tu carrera profesional /laboral.
Good to know
Good to know
Know what's good ,
what to watch for , 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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Save Fundamentos de sistemas recomendadores to your list so you can find it easily later:
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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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Revisit collaborative filtering
Show steps
Review the basic concepts and techniques of collaborative filtering to strengthen your foundation.
Browse courses on
Collaborative Filtering
Show steps
Show steps
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
Show steps
Engage in targeted practice to improve your problem-solving skills and reinforce your understanding.
Show steps
Show steps
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
Show steps
Seek out and follow tutorials to enhance your understanding of cutting-edge recommendation methods.
Browse courses on
Matrix Factorization
Show steps
Show steps
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
Show steps
Create a functional prototype of a recommendation system to apply and test your knowledge.
Browse courses on
Recommendation Systems
Show steps
Show steps
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 Developer
Software Developers may work on developing recommender systems. This course may be helpful for learning how recommender systems are built.
Data Scientist
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.
Machine Learning Engineer
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.
Statistician
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.
Quantitative Analyst
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.
Product Manager
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.
Data Analyst
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.
User Experience Researcher
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.
Business Analyst
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.
Marketing Manager
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.
Sales 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.
Technical Writer
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.
Customer Success Manager
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.
Quality Assurance Analyst
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.
Information Security 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.
For more career information including salaries, visit:
OpenCourser.com/course/8g4ygw/fundamentos
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.
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.
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.
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.
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.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8g4ygw/fundamentos
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Effort
4 horas/semana
Level
Intermediate
Via
Coursera
Institution
Pontificia Universidad Católica de Chile
Instructor
Denis Parra
Language
Español
Good to know
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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