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很高兴这次最终获得了Hackathon的冠军！我们音乐预测算法的精度达到了RMSE=13.24598

SVD++的implicit feedbacks这次并没有发挥很大的作用，相反，words里的86列feature有很大的帮助，用在了Logistic Regression中，另外，user的各种profile和item，artist的关系组合，也带来了很好的效果 比赛介绍如下：

COMPETITION GOAL

Can 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 ; 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  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 particular 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  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 

hashtag #musicdata #ds_ldn #DSGhack

 

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