This is a proposal to help MusicBrainz use its internal data to generate relationships between artists. This will mainly help in recommending similar artists to a user who is currently viewing an artist’s description. The deliverable will consist of a Collaborative Filtering algorithm which can be integrated into the existing MusicBrainz system and which will help MusicBrainz utilize the internal data it has collected over the years. In addition to the project code, I will contribute in developing a Rating System which will serve a two-fold purpose - accumulate valuable data to feed into the CF algorithm, and, help users rate and comment on Artists/Albums/Compositions, thereby helping other users know about the Artist/Album/Composition better.
MusicBrainz provides a good music metadatabase using the details provided by the user community. One of the many features it provides is an option to find out artists who are similar musically to the artist the user is currently reading about. This helps users find out more about artists they might not have known before, and even if they did know, they can find out more about the related artists(like the albums released, biography of the artist etc). Currently, MusicBrainz is relying on external data to determine the similarity between artists. Instead, it would be better to use the internal data collected over the years in its database and calculate the similarity of artists as it would not only remove the dependency on an external data source, but also would take the preferences and tastes of the users of the community into consideration.
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