BPMFARD: A Bayesian Probabilistic Matrix Factorization Algorithm with Automatic Rank Determination in Recommender Systems

Matrix factorization is a prevalent and effective technique for building recommender systems. However, traditional matrix factorization methods demand manual setting and tuning of hyperparameters, like regularization coefficients, learning rates, and the dimension of the feature matrix rank . To automate this, we introduce BPMFARD, a Bayesian probabilistic matrix factorization algorithm with automatic rank determination. By setting a prior distribution for factor matrices and devising an effective parameter elimination strategy, BPMFARD enables automatic parameter adjustment during training to significantly alleviate overfitting, enhancing recommendation accuracy. Experiments with benchmark datasets show that BPMFARD outperforms the benchmark methods. Since matrix factorization can be seen as a simple neural network, the rank determination strategy in matrix factorization may provide a valuable and interesting research perspective for the embedding size learning in neural collaborative filtering.