Similar Sounding Music [closed]

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We have a user, with a music library of 100 songs. Out of those he loves 20, he hates 10 and there are 5 he neither hates nor loves. He never listened to the remaining 65.

Question: What kind of algorithm(s) is/are used to scan the remaining 65 songs and find out music the user will like?

Answer 1

To suggest new unfamiliar content to a user, the general approach is to use machine learning, specifically collaborative filtering, which is often used for recommender systems. The idea is to use the knowledge of the crowd, and finds people (or groups) that have similar taste to yours, and recommend new items that they tend to like.

An alternative is creating a classification algorithm for like/dislike, but that might require extracting features from each song that will describe the essense of the problem, and that's usually not trivial at all.

Some classification algorithms you might want to try are SVM, Naive Bayes, neural networks, Decision trees and more. The real challenge, as I mentioned would be to find the right features for the problem.

Answer 2

Do some research on a product called MusicIP, it had some very clever algorithm fingerprinting technology. It converted the track to WAV and then created a fingerprint, then some clever magic to match songs that were similar.