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Nearest Neighbor Collaborative Filtering

Recommender Systems,

In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user. You will explore and implement variations of the user-user algorithm, and will explore the benefits and drawbacks of the general approach. Then you will learn the widely-practiced item-item collaborative filtering algorithm, which identifies global product associations from user ratings, but uses these product associations to provide personalized recommendations based on a user's own product ratings.
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Rating 3.7 based on 58 ratings
Length 5 weeks
Starts Jul 3 (42 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

collaborative filtering

All in all, it is a comprehensive introduction to collaborative filtering.

Provides a good overview of item based and user based collaborative filtering approaches.

Diverse content that helps in understanding the basic concepts of collaborative filtering.

Great learning experience about collaborative filtering!

I found this course very informative and clears lot of concept in Item based and used based collaborative filtering.

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recommender systems

Thank you for this course -- it opened my eyes to the universal applicability of recommender systems in tech applications.

Excellent course providing not only the knowledge of algorithms but also useful insights into developing and maintaining recommender systems.

Regurgitating information found in required readings shows no level of comprehension of course material and is a severe disservice to students.I will hope for better general coverage of recommender systems in the future in another course.

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spreadsheet assignment

Spreadsheet assignment in week 4 is poorly designed (as evidenced by many forum threads with people not knowing what is it that the authors actually want).

Spreadsheet assignment on Week 3 is the main reason I rate this course so low, and a lot of people on discussion forums agree with me on assignment quality Week 4 assignments can do with a bit more clarity.

Spreadsheet assignment helped me to clearly understand the algorithms.

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very good

Loved it...many thanks Prof. Joe for bringing this content to Coursera a great class, I learned some insight in these algorithms Very good course, there is a glaring error in Week 4s assignment.

But if you check the forums it can be easily solved Very satisfied to do this, the videos are too long, very good quality and a lot of practical information.I love it!

Very good content !

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little bit

Forum is also a little bit deserted, although contains some critical hints to pass the assignments (such a hints worth to be included in the assignment descriptions itself).

Rather non-technical, interesting general information, plus voluntary programming assignment which I personally found little bit "bulky".

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very useful

However the exercise needs a bit more work to be very useful.

I think this is very useful for introductory, but it lacks some references for who wants go deeper.

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algorithms but

I also wish the lectures cover actual mathematical examples to work us through the algorithms Thank you so very much to open my eye see more view of recommendation field not only algorithms but use case and many trouble-shooting in worldwide business, moreover interview with noble professor.

good introduction to topics and algorithms but very little help provided for the assignment in clarifying doubts in forums and unclear explanations were given for assignments.

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Rating 3.7 based on 58 ratings
Length 5 weeks
Starts Jul 3 (42 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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