Introduction to Recommender Systems
Non-Personalized and Content-Based
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Rating | 4.2★ based on 92 ratings |
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Length | 5 weeks |
Effort | 4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track. |
Starts | Jun 26 (47 weeks ago) |
Cost | $79 |
From | University of Minnesota via Coursera |
Instructors | Joseph A Konstan, Michael D. Ekstrand |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Data Science Business Programming |
Tags | Data Science Business Marketing Machine Learning |
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What people are saying
recommender system
Thank you for your course, very Helpfull for those who are keep in touch with recommender System engine.
I particularly like that insights are provided in terms of what aspects to consider when designing a recommender system; pros and cons of different approaches.
Good course for basic intro to recommender system.
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recommender systems
Great introduction to Recommender systems.
Informative and helpfull for me as recommender systems practitioner.
You should talk about music recommender systems as well!
Pros:Some useful terminology if you want to ever communicate with someone who does recommender systems.Cons:Very diluted content.Mostly large text slides with the presenter talking in a monotone voice.Programming exercises are done in Java and require deploying an IDE + an unused open source project developed by the authors.
Rather than history of Recommender systems & what happened in the 90s, I would have been happier if the course was able to throw light on the latest stuff in this field, the latest mathematical techniques etc.
pues esta bien chido el curso 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.
The course es really helpfull to understand how the recommender system works and what points yo have to take care when you have to implement The course authors did a great job explaining concepts related to recommender systems.
it's a fantastic course that gives you a good idea of what the objectives of recommender systems are and some intuition on the way how it can be accomplished.
It would be nice to have a hierarchical overview of the recommender systems.
Lectures include interviews with people who have successfully implemented recommender systems in their products or who are researching the permutations, challenges and extensions to recommender system development.
Not only does the course provide the chance to build your own recommender systems (optional) but also highlights the complexities and opportunities for refining and improving recommendations.
The guest interviews were also superb and gave me exposure to different areas of research in recommender systems in general.
This course is a wonderful logical informative introduction to several basic types of recommender systems.
A good introduction to the basic concepts of recommender systems.
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course is really
A part from that, the course is really awesome.
This course is really helpful in understanding the state of the art of non-personalized and content-based recommender systems.
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recommendation systems
An excellent in-depth introduction into the concepts around recommendation systems!
Really good course to get started with recommendation systems!
I think this is a good course to start exploring recommendation systems.
I highly recommend this course to anyone building recommendation systems.
I am overly critical though, all in all a very good way to understand recommendation systems.
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content based
Ended up confusing with various interviews and what are differences between various content based recommenders.
It's a good introduction to content based and non-personailzed recommender systems.
a good course with detail explanation on many aspect of non-personalized and content based recommendations.
Overall a good course that teaches the basics for content based recommenders.Would be great if the assignments were a bit more challenging, e.g.
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data scientists
which are basically language of choice for data scientists and also easy to have grasp on for newbies.
Simple reason is that a lot of budding data scientists are not coming from CS background and dont have necessary skillset in Java.
Uses simplistic tools that don't scale to data applications or otherwise dated tools not really used by data scientists or machine learning engineers making exercises either simplistic or a waste of time.
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honors track
Pretty good Well-designed assignments and instructive programming exercises in the honors track.
Well structured classes, good explanations and incredible interviews I love the course's content but discussions are of poor quality and the honors tracks assignments are a little messy.
Would be great if the solutions of the honors track should be available to those who want to learn and not just for the sake of getting certificate 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 Too basic and too repetitive (the videos could be half as long) Fantastic course.
One major drawback of this course is that the honors track is not implemented in Python, though I believe that possibly in future versions this will be adapted.
In my case, the two options left are either I learn Java programming or I do not take the honors track.
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python or
There is a need to have this course in Python or some other statistical programming language.
Some assignments were a little bit awkward but overall they Was expecting programming activities in Python or R, not in Java =/ This course mostly works.
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too long
videos are too long...
the video is too long!
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very slow
The introduction is very slow in my opinion.
However I'm also extremely bored watching the videos because looking at the lectures reading the scripts (most of the time with very slow speed) is one of the quickest way to send people to sleep.
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Rating | 4.2★ based on 92 ratings |
---|---|
Length | 5 weeks |
Effort | 4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track. |
Starts | Jun 26 (47 weeks ago) |
Cost | $79 |
From | University of Minnesota via Coursera |
Instructors | Joseph A Konstan, Michael D. Ekstrand |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Data Science Business Programming |
Tags | Data Science Business Marketing Machine Learning |
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