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A kernel is a precomputed distance function. This means that instead of computing the distances between pairs of points every time you need them, you'll just compute them all a single time, at the start, and cache the values in memory. This is a great idea if the dimensionality of your data is very high, and you expect to have to make a lot of distance ...


That is not what an auto-encoder is doing. An auto-encoder gives you a compressed representation of the input. It is trained by mapping the input data to itself, with the compressed form in between. To predict recommendations, you need to train your input data on existing user recommendations.

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