4.4
estrellas
35 calificaciones

Paolo Cremonesi

2360 ya inscrito

Ofrecido Por

EIT Digital

Politecnico di Milano

Basic Recommender Systems

EIT Digital

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This course introduces you to the leading approaches in recommender systems. The techniques described touch both collaborative and content-based approaches and include the most important algorithms used to provide recommendations. You'll learn how they work, how to use and how to evaluate them, pointing out benefits and limits of different recommender system alternatives. 
After completing this course, you'll be able to describe the requirements and objectives of recommender systems based on different application domains. You'll know how to distinguish recommender systems according to their input data, their internal working mechanisms, and their goals. You’ll have the tools to measure the quality of a recommender system and to incrementally improve it with the design of new algorithms. You'll learn as well how to design recommender systems tailored for new application domains, also considering surrounding social and ethical issues such as identity, privacy, and manipulation.

Providing affordable, personalised and high-quality recommendations is always a challenge! This course also leverages two important EIT Overarching Learning Outcomes (OLOs), related to creativity and innovation skills. In trying to design a new recommender system you need to think beyond boundaries and try to figure out how you can improve the quality of the predictions. You should also be able to use knowledge, ideas and technology to create new or significantly improved recommendation tools to support choice-making processes and strategies in different and innovative scenarios, for a better quality of life.

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100 % en línea

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Nivel intermedio

Basic notions of linear algebra

Aprox. 11 horas para completar

Inglés (English)

¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?

Prueba Coursera para negocios

Qué aprenderás

You'll be able to build a basic recommender system.
You'll be able to choose the family of recommender systems that best suits the kind of input data, goals and needs.
You'll learn how to identify the correct evaluation activities to measure the quality of a recommender system, based on goals and needs.
You'll be able to point out benefits and limits of different techniques for recommender systems in different scenarios.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Nivel intermedio

Basic notions of linear algebra

Aprox. 11 horas para completar

Inglés (English)

¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?

Prueba Coursera para negocios

Instructor

Calificación del instructor

4/5 (6 calificaciones)

Paolo Cremonesi

Associate Professor

Department of Electronics, Information and Bioengineering - Politecnico di Milano

3927 alumnos

2 cursos

Ofrecido por

EIT Digital

EIT Digital is a European education and innovation organisation with a mission to foster digital technology innovation and entrepreneurial talent for economic growth and quality of life. By linking education, research, and business, EIT Digital empowers digital top talent for the future.

EIT Digital provides online and face-to-face Innovation and Entrepreneurship education to raise quality, increase diversity and availability of the top-level content provided by 20 leading technical universities around Europe. The universities deliver a unique blend of the best of technical excellence and entrepreneurial skills and mindset to digital engineers and entrepreneurs at all stages of their careers. The academic partners support Coursera’s bold vision to enable anyone, anywhere, to transform their lives by accessing the world’s best learning experience. This means that EIT Digital gradually shares parts of its entrepreneurial and academic education programmes to demonstrate its excellence and make it accessible to a much wider audience. EIT Digital’s online education portfolio can be used as part of blended education settings, in both Master's and Doctorate programmes, and for professionals as a way to update their knowledge.

Politecnico di Milano

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Programa - Qué aprenderás en este curso

Semana1

Semana 1

3 horas para completar

BASIC CONCEPTS

In this first module, we'll review the basic concepts for recommender systems in order to classify and analyse different families of algorithms, related to specific set of input data. At the end, you’ll be able to choose the most suitable type of algorithm based on the data available, your needs and goals. Conversely, you'll know how to select the input data based on the algorithm you want to use.

3 horas para completar

11 videos (Total 29 minutos), 2 lecturas, 2 cuestionarios

11 videos

Course overview and welcome by the instructor2mWelcome by the instructor - module overview1mIntroduction to Recommender Systems3mTaxonomy of Recommender Systems6mItem-Content Matrix1mUser-Rating Matrix2mInferring Preferences3mRecap by the instructor1mNon-personalized Recommender Systems3mGlobal Effects2mConclusions by the instructor1m

2 lecturas

Course Syllabus10mCredits & Acknowledgements5m

1 ejercicio de práctica

Module 1 - Graded Assessment45m

Semana2

Semana 2

3 horas para completar

EVALUATION OF RECOMMENDER SYSTEMS

3 horas para completar

12 videos (Total 35 minutos)

Semana3

Semana 3

3 horas para completar

CONTENT-BASED FILTERING

In this module we’ll analyse content-based recommender techniques. These algorithms recommend items similar to the ones a user liked in the past. We’ll review different similarity functions and you’ll then be able to choose the more suitable one for your system. The main input is the Item-Content Matrix (ICM) which describes all the attributes for each item. We’ll see how we can improve the quality of content-based techniques, by normalising and tuning the importance of each attribute in the ICM: you’ll be able to use some specific tuning strategies in order to obtain the best quality recommendations from your system. So, at the end of this module, you’ll know how to build a content-based recommender system, how to clean and normalize your input data.

3 horas para completar

9 videos (Total 19 minutos)

Semana4

Semana 4

3 horas para completar

COLLABORATIVE FILTERING

In this module we’ll study collaborative filtering techniques, which use the User Rating Matrix (URM) as the main input data, describing the interaction between users and items. We’ll learn how to build non-personalised recommender systems and how to normalise the URM, in order to provide better recommendations. At the end of the module you’ll be able to select the most appropriate similarity function and the most suitable way to compute similarity, overcoming issues related to explicit ratings.

3 horas para completar

9 videos (Total 42 minutos)

Reseñas

4.4

5 reseña

5 stars
71,42 %
4 stars
17,14 %
2 stars
2,85 %
1 star
8,57 %

Principales reseñas sobre BASIC RECOMMENDER SYSTEMS

por RT24 de oct. de 2020

There is a nice introduction to recommender systems field

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Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si compro el Certificado?
Cuando compras un Certificado, obtienes acceso a todos los materiales del curso, incluidas las tareas calificadas. Una vez que completes el curso, se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes participar del curso como oyente sin costo.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.

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Basic Recommender Systems

Basic Recommender Systems

Acerca de este Curso

Qué aprenderás