Toward Auto-Learning Hyperparameters for Deep Learning-Based Recommender Systems

Bo Sun, Di Wu, Mingsheng Shang, Yi He

Abstract: Deep learning (DL)-based recommendation system (RS) has drawn extensive attention during the past years. Its performance heavily relies on hyperparameter tuning. However, the most common approach of hyperparameters tuning is still Grid Search—a tedious task that consumes immerse computational resources and human efforts. To aid this issue, this paper proposes a general hyperparameter optimization framework for existing DL-based RSs based on differential evolution (DE), named DE-Opt. Its main idea is to incorporate DE into a DL-based RS model’s training process to auto-learn its hyperparameters λ (regularization coefficient) and η (learning rate) simultaneously at layer-granularity. Empirical studies on three benchmark datasets verify that: 1) DE-Opt is compatible with and can automate the training of the most recent DL-based RSs by making their λ and η adaptively learned, and 2) DE-Opt significantly outperforms the state-of-the-art hyperparameter searching competitors in terms of both higher learning performance and lower runtime.