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I have to find the best 10 items from the set of items for x number of given features. I don't know what the best recommendation will be. User data is not available to validate it. After reading a number of papers I came to know about voting algorithm.

Trying to implement something like this-

  1. Find x number of features for static variables( that doesn't change over time), dynamic variables (that change very frequently).
  2. Assume some weights for the weights on the basis of requirement for dynamic variables.
  3. Try to come up with 3-4 regression models by combining the static and dynamic variables and find the best 10 items from each
  4. Take the common one out, and then calculate the error.
  5. Back-propagate the error to get the new weights.
  6. Keep on iterating till we get at-least common top 8 elements.

Is the approach right?

Any guidance for what to take as error.

My approach to error calculation was-

  1. Mean of common items feature values (X).
  2. For each model MSE where mean is X and Xi is feature values for uncommon items of that model.

I am new to it. Can anyone please guide me through it.

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