A music recommendation system using fuzzy logic and Last.fm tags

Running the programs

1. Download & extract the dataset
2. run python preprocess/split_tsv_per_user.py <path-to-tsv-file>
3. run run_all.sh <path-to-split/-directory> and follow the instructions
4. Evaluation scripts are in eval/, data used in the report can be found in paper_data FLS/ contains a basic implemenation of fuzzification, which we're not using. It's just a try-out, and can be disregarded

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Dataset:

• http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/lastfm-1K.html

Useful links:

• Last.fm API in Python: pylast
• Last.fm API documentation: last.fm/api
• cmeans clustering(from scikit-fuzzy)
• ishibuchi classification maybe?

Note that the Last.fm API is veeery slow, so be doing the preprocessing yourself would take a lot of time It'd probably be wise to work with configuration files in the future.._{^{also the code could be cleaned up a whole bunch}}

Defuzzification (and returning albums based on clusters of tags)

• Get an album from other user in the same cluster(collaborative) - but then why cluster and not just lookup the closest user?
• Use the most frequent tag(s) with tag.getTopAlbums. Will be very prone to music coming from the head of popularity
• Something with difference to closest cluster(weighted in case of fuzzy clusters)

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The .api_key file should look like a json file:

{
  "application_name": "Fuzzy Logic Music Recommendation",
  "key": <GET FROM LAST.FM>,
  "shared_secret": <GET FROM LAST.FM>,
  "username": <INSERT USERNAME HERE>,
  "password": <INSERT PASSWORD HERE>
}