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Music Recommender System Using Pyspark に戻る

Coursera Project Network による Music Recommender System Using Pyspark の受講者のレビューおよびフィードバック

4.5
14件の評価
4件のレビュー

コースについて

Nowadays, recommender systems are everywhere. for example, Amazon uses recommender systems to suggest some products that you might be interested in based on the products you've bought earlier. Or Spotify will suggest new tracks based on the songs you use to listen to every day. Most of these recommender systems use some algorithms which are based on Matrix factorization such as NMF( NON NEGATIVE MATRIX FACTORIZATION) or ALS (Alternating Least Square). So in this Project, we are going to use ALS Algorithm to create a Music Recommender system to suggest new tracks to different users based upon the songs they've been listening to. As a very important prerequisite of this course, I suggest you study a little bit about ALS Algorithm because in this course we will not cover any theoretical concepts. Note: This project works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions....
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Music Recommender System Using Pyspark: 1 - 4 / 4 レビュー

by Mariana L F D A

2020年12月22日

The instructor is great but the course is impossible to complete as the dataset is not available. Other students had the same issue and it was not solved apparently.

by MAHESH M

2021年11月24日

good

by Li J

2021年3月3日

Regarding to the other review says No dataset, actually, you can type the google drive link of the dataset by yourself, the link is showed in the video.

by Garigipati P

2021年10月6日

easy to learn these guided projects