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返回到 Introduction to Recommender Systems: Non-Personalized and Content-Based

学生对 University of Minnesota 提供的 Introduction to Recommender Systems: Non-Personalized and Content-Based 的评价和反馈

4.4
654 个评分

课程概述

This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations. After completing this course, you will be able to compute a variety of recommendations from datasets using basic spreadsheet tools, and if you complete the honors track you will also have programmed these recommendations using the open source LensKit recommender toolkit. In addition to detailed lectures and interactive exercises, this course features interviews with several leaders in research and practice on advanced topics and current directions in recommender systems....

热门审阅

YW

Nov 2, 2016

I think this is an amazing course for beginners who are interested in recommender systems, I strongly recommend this course to the students and engineers who are working on recommender systems.

BS

Feb 12, 2019

One of the best courses I have taken on Coursera. Choosing Java for the lab exercises makes them inaccessible for many data scientists. Consider providing a Python version.

筛选依据:

51 - Introduction to Recommender Systems: Non-Personalized and Content-Based 的 75 个评论(共 138 个)

创建者 thomas l

Jul 21, 2018

I think I am on the right track to changing my career from java engineer from data scientist, this course is one of the best start point

创建者 Francisco C

Mar 20, 2017

Excelente curso, presenta una vista amplia de técnicas para la implementación de sistemas de recomendación, lo recomiendo totalmente.

创建者 Abhijith R

Aug 30, 2020

Great intro to recommendation systems, the course is well structured and engaging to all students of different backgrounds.

创建者 Тефикова А Р

Oct 5, 2016

Курс очень понравился, спасибо большое за такую уникальную возможность вникнуть в суть рекомендательных систем!

创建者 Saurabh D

Aug 13, 2023

Great course.

I would encourage the authors of the course to replace Java with Python in the Honors track

创建者 Chris C

Jul 6, 2021

Excellent content, great structured frameworks to understand when / why to use different recommenders

创建者 Patrick D

Jun 25, 2017

Great, thorough introduction with tracks for both Java programmers and non-programmers.

创建者 Pankaj M

Dec 20, 2022

Well designed introduction to the formal concepts and analysis of Recommender systems

创建者 Kevin R

Oct 8, 2017

Well-designed assignments and instructive programming exercises in the honors track.

创建者 Ashwin R

Jun 26, 2017

An excellent in-depth introduction into the concepts around recommendation systems!

创建者 Santiago F

Feb 1, 2021

Muy claro y de gran ayuda para los que se estén introduciendo en el tema.

创建者 Xinzhi Z

Jul 17, 2019

Great course. I really appreciated the efforts spent by the course team.

创建者 王涛

Apr 10, 2019

Really Good! I think it will be helpful to me and take a job for me!

创建者 Light0617

Jul 18, 2017

great!! Let me better understand the research and practical fields!

创建者 Sushmita B

Jun 7, 2020

The course is very good and the course instructor is excellent .

创建者 Luis D F R

Apr 17, 2017

Really good course to get started with recommendation systems!

创建者 Dan T

Oct 31, 2017

great overview of the breadth of material to get started

创建者 Sreenath A

Jun 29, 2017

Excellent course taught in simple language.

创建者 Biswa G S

Mar 28, 2018

Good overview on the recommend-er system.

创建者 Sherry L

Nov 21, 2017

great professors and inspiring lectures!

创建者 王嘉奕

Nov 6, 2019

Excellent course which helps me a lot.

创建者 su l

Aug 23, 2019

great course, learnt a lot, thanks!

创建者 Fernando C C

Nov 7, 2016

pues esta bien chido el curso

创建者 Son

Jan 19, 2019

good exercises & lectures