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chingweichen

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A dataset and open-ended challenge for music recommendation research

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graded 80810

Predict if users will skip or listen to the music they're streamed

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Amazon KDD Cup '23: Multilingual Recommendation Ch

What kind of interactions are reflected in the sessions training set?

About 1 year ago

Hi,

Looking at the session_training.csv, it has the following columns:

  • Previous items: Array of item IDs previously interacted with
  • Next item: Item ID of next item interacted with (after the previous items)
  • Locale

I couldn’t find any documentation on what an β€œinteraction” is - is it a purchase? is it a click? Are all interactions the same, or are some of them different? This is important to know when building a model.

Thanks!

Spotify Million Playlist Dataset Challenge

Any way to get access to this dataset for teaching?

Almost 2 years ago

Hi tommcd09 and nathan_carter. From the terms and conditions, the β€œChallenge Result” is defined as:
β€œany result, outcome, creation, submission, or other output, arising from your use of the Spotify Data, whether or not publicly available or shared with Spotify.”
The intention is that most non-commercial research and educational use cases fall under this definition, and should be allowed by the terms. Please note that redistributing the dataset is prohibited, access is only through the AICrowd website.
Thanks for your interest, I look forward to seeing the result of your and your student’s work!

Adding Spotify Million Playlist Dataset in Kaggle(for computing)

Over 3 years ago

Hi md_sadakat_hussain_f,

Thanks for asking, I understand that it is a little extra work (and cost) to work on this dataset with your own machines. For free computing, I would also recommend Google Colab - for storage, you could store the ZIP file in Google Drive (you get 15GB free) and you can copy the ZIP file from the Colab instance and unzip it on the ephemeral storage (!unzip filename.zip), then process it there. It might be faster than uploading from your local machine each time, and easier to repeat if you script it.

Alternately, you could also follow these instructions from fast.ai (Steps 1-3) on setting up a GCP account and getting a VM and Jupyter notebook. You should get $300 credit for free as a new GCP user. Some VM’s are less than $1/hour, and include GPU and 100GB persistent disk.

Of course there are many other options for low or no-cost computing on different platforms, perhaps others can post what works for them here?

Please do respect the terms of the dataset license - you may not redistribute the dataset on Kaggle or other public platforms.

chingweichen has not provided any information yet.