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I am curious as to how will you define "quality" here? Is it based on wisdom of the crowds or expert reviews or your own personal consumption pattern?

• SomeStupidPoint 9y ago
  Could you use something like a convolutional deep net -- in it should have similar low-level features to things you have read, but low scores on higher parts of the network? I mean, if you generated one for each class of people, you'd probably find that there are easier statistical trends, but conceptually. In fact, now that I think about it a bit more, you likely could use something like the Netflix algorithm and very broadly read people, who overlap on what you have read, then look for books they've read that score lowest on similarity to things you've read based on inverting the scoring mechanism. It wouldn't be perfect, but with some tuning you could probably make that work as a simpler model than the DNN one.
  • nocoder 9y ago
    What you are saying sounds similar to collaborative filtering[0], which afaik is widely used by likes of amazon. The main challenge I find with the collaborative filtering approach is that it gives lot of weight to my recent behavior so it gets boring very quickly. Not to mention the fact that it starts restricting me to narrow genres & inhibiting the discovery of something new & novel. [0] - https://en.wikipedia.org/wiki/Collaborative_filtering https://en.wikipedia.org/wiki/Collaborative_filtering