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Multiplying with alpha (a constant to specify the rate of approaching the minimum) in the update rule in matrix factorizaiont



2019 Community Moderator ElectionHow to factorize the Matrix in TensorFlow? (Recommender System)Having difficult interpreting the eigenvectors for a simple 3x2 matrix










0












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Where does the negative sign go after multiplying with alpha (a constant to specify the rate of approaching the minimum) in the update rule in matrix factorization?



Squared error = (actual rating - predicted rating)$^2$



Now if I take a partial derivative with respect to two variables, I get a negative sign. However, in the update rule of these two variable, we multiply with alpha (a constant to specify the rate of approaching the minimum). In different literatures, I have found a 'positive' sign appearing in the update rule. Shouldn't it be negative instead?










share|improve this question











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    0












    $begingroup$


    Where does the negative sign go after multiplying with alpha (a constant to specify the rate of approaching the minimum) in the update rule in matrix factorization?



    Squared error = (actual rating - predicted rating)$^2$



    Now if I take a partial derivative with respect to two variables, I get a negative sign. However, in the update rule of these two variable, we multiply with alpha (a constant to specify the rate of approaching the minimum). In different literatures, I have found a 'positive' sign appearing in the update rule. Shouldn't it be negative instead?










    share|improve this question











    $endgroup$














      0












      0








      0





      $begingroup$


      Where does the negative sign go after multiplying with alpha (a constant to specify the rate of approaching the minimum) in the update rule in matrix factorization?



      Squared error = (actual rating - predicted rating)$^2$



      Now if I take a partial derivative with respect to two variables, I get a negative sign. However, in the update rule of these two variable, we multiply with alpha (a constant to specify the rate of approaching the minimum). In different literatures, I have found a 'positive' sign appearing in the update rule. Shouldn't it be negative instead?










      share|improve this question











      $endgroup$




      Where does the negative sign go after multiplying with alpha (a constant to specify the rate of approaching the minimum) in the update rule in matrix factorization?



      Squared error = (actual rating - predicted rating)$^2$



      Now if I take a partial derivative with respect to two variables, I get a negative sign. However, in the update rule of these two variable, we multiply with alpha (a constant to specify the rate of approaching the minimum). In different literatures, I have found a 'positive' sign appearing in the update rule. Shouldn't it be negative instead?







      matrix-factorisation matrix






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Apr 1 at 4:11









      Ethan

      695425




      695425










      asked Apr 1 at 2:52









      Nafiz HNafiz H

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