eNet regularized BCS Framework for collaborative filtering
This code solves the problem of predicting missing ratings in a recommender system by casting the problem of matrix factorization in blind compressive sensing framework with elastic net regularization.
minimize_(U,V) ||Y-A(UV)||_2 + lambda_3||U||_F + lambda_1||V||_1 + lambda_2||V||_F
Here U and V are user and item latent factor matrices.
The algorithm uses Majorization-Minimization approach to solve the above formulation in an efficient manner.
The demo file contains one test-train pair for the 100K movielens data set (available at http://grouplens.org/datasets/movielens/)
Cite As
Anuprriya Gogna (2024). eNet regularized BCS Framework for collaborative filtering (https://www.mathworks.com/matlabcentral/fileexchange/47139-enet-regularized-bcs-framework-for-collaborative-filtering), MATLAB Central File Exchange. Retrieved .
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- MATLAB > Mathematics > Sparse Matrices >
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eNet-BCS/
Version | Published | Release Notes | |
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1.0.0.0 |