Won the EMI Music recommendation hackathon Championship

I'm glad to have won the hackathon championship this time! The accuracy of our music prediction algorithm reaches RMSE = 13.24598.

The implicit feedbacks of SVD ++ did not play a major role this time. On the contrary, 86 feature columns in words are of great help and used in logistic regression. In addition, the combination of various profiles, items, and artist of users also brings good results.

The competition is described as follows:

Competition goalcan you predict if a listener will love a new song?

"Soulful"... "Catchy"... "cool"... "cheesy"... "edgy"

How
Do people connect to and describe the music they have just heard?

Emi
Insight performs extensive market research about their artists by interviewing thousands of people around the world. This research has produced Emi
One million interview dataset; one of the largest music preference datasets in the world today, that connects data about people -- who they are, where they live, how they engage with music in their daily lives -- with their opinions about EMI's artists.

This data
Science London hackathon will focus on one key subset of this data: understanding what it is about people and artists that predicts how much people are going to like a special track. we have taken a sample of the data from the United Kingdom that provides
A granular mixture of profile, word-Association, and rating data.

The
Goal of this weekend hackathon is to design an algorithm that combines Users '(a) demographics, (B) artist and track ratings, (c) answers to questions about their preferences for music, and (d) words that they use to describe EMI artists in order to predict
How much they like tracks they have just heard.

There
Is also a visualization thread
Where you can submit your most amazing music-data viz and view and vote on other contestants 'entries. go to 'prospect 'At the top of this page. submissision will open at the same time as the competition.

(Data
Will be made available 24 hours prior to the start of the contest)

For
More Info http://musicdatascience.com/

Hashtag
# Musicdata # ds_ldn
# Dsghack

 

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