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The approach you describe is one of many. Traditional collaborative filtering using matrix factorization will "discover" the features you listed, or other non-i

The approach you describe is one of many. Traditional collaborative filtering using matrix factorization will "discover" the features you listed, or other non-intuitive features, from patterns in the data. They don't always need to be listed explicitly, and training individual asymmetric models (like your one NN per person) isn't always ideal. Per person models won't make a lot of sense unless each person has rated a large number of songs, which usually isn't the case. In general, methods like this require an epic amount of data to work well for consumers. The Netflix Prize made a dataset with 110 million ratings available, and that was barely enough to make impressive predictions. The issue here isn't the statistical model being used, but the paucity of training data. If you trained this or any other model on one billion rated songs, the quality of the recommendations could blow your mind. They would however still skew toward the average musical tastes.

Qantourisc 10y ago
Ow the NN would be rather small though: like 1 or 3 layer kind. The bulk of the identification has to be done before. Although music patterns could be a problem. And the skew toward the average musical taste is exactly my issue. Finding music is more like playing bingo to me. Disclaimer: I haven't used any note-wordy NN yet, so this might still required a ton of input. But if the NN could start recognizing thinks like for example: likes piano with male singer, but a penalty when it includes a violin in these genres (stupid example) it would IMO be an improvement.
moultano 10y ago
>Traditional collaborative filtering using matrix factorization will "discover" the features you listed, or other non-intuitive features, from patterns in the data. I don't think that has turned out to be the case. Most people's listening habits follow genre, and just don't have that much latent structure to extract.

Qantourisc 10y ago

Ow the NN would be rather small though: like 1 or 3 layer kind. The bulk of the identification has to be done before. Although music patterns could be a problem. And the skew toward the average musical taste is exactly my issue. Finding music is more like playing bingo to me. Disclaimer: I haven't used any note-wordy NN yet, so this might still required a ton of input. But if the NN could start recognizing thinks like for example: likes piano with male singer, but a penalty when it includes a violin in these genres (stupid example) it would IMO be an improvement.

moultano 10y ago

>Traditional collaborative filtering using matrix factorization will "discover" the features you listed, or other non-intuitive features, from patterns in the data. I don't think that has turned out to be the case. Most people's listening habits follow genre, and just don't have that much latent structure to extract.