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學生對 Coursera Project Network 提供的 Music Recommender System Using Pyspark 的評價和反饋

15 個評分
4 條評論


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....

1 - Music Recommender System Using Pyspark 的 4 個評論(共 4 個)

創建者 Mariana L F d A


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.




創建者 Li J


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.

創建者 Garigipati P


easy to learn these guided projects