Created
August 12, 2015 13:30
-
-
Save alksl/25286630bdec164dbfea to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| function [X,Y] = trainLFM(P, f, alpha, lambda) | |
| %trainLFM Trains Latent Factor Model for CF with implicit feedback | |
| % Takes a playcount matrix P and its parameters alpha and lambda. | |
| % and uses the C_{u,s} = 1 + \alpha P_{u,s} as confidence model | |
| [num_users, num_songs] = size(P); | |
| % Initialize decomposisiton X and Y with values from 0 to 1 | |
| X = rand(num_users, f); | |
| Y = rand(num_songs, f); | |
| % TODO add convergence check. | |
| % Update the user rows with the least squares expression | |
| % x_{u} = (Y^{T} C^{u} Y - \lambda I)^{-1} X^{T} C^{u} t_{u} | |
| % Use this equality to precompute Y^{T} Y for all users | |
| % Y^{T} C^{u} Y = Y^{T} Y + Y^{T} (C^{u} - I) Y | |
| A = Y'*Y; | |
| % Update all users | |
| parfor u = 1:num_users | |
| AU = A + (Y' * diag(alpha*P(u,:)) * Y) - lambda * eye(f); | |
| tu = (P(u,:) > 0)'; | |
| b = Y' * (diag(alpha*P(u,:)) + diag(tu)) * tu; | |
| X(u,:) = AU\b; | |
| end | |
| % Update the song rows with the least squares expression | |
| % y_{s} = (X^{T} C^{s} X - \lambda I)^{-1} X^{T} C^{s} X^{T} t_{s} | |
| % Use this equality to precompute Y^{T} Y for all users | |
| % X^{T} C^{u} X = X^{T} X + X^{T} (C^{s} - I) X | |
| A = X'*X; | |
| parfor s = 1:num_songs | |
| AS = A + (X' * diag(alpha * P(:,s)) * X) - lambda * eye(f); | |
| ts = (P(:,s) > 0); | |
| b = X' * (diag(alpha * P(:,s)) + diag(ts)) * ts; | |
| Y(s,:) = AS\b; | |
| end | |
| end |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment