Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering
This work addresses the need for more efficient and effective automated machine learning in collaborative filtering, though it is incremental by building on existing AutoML techniques.
The paper tackles the problem of jointly optimizing hyperparameters and architectures for collaborative filtering models, which is challenging due to large search spaces and high evaluation costs, and achieves better performance compared to hand-designed and previously searched models.
Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either search architectures or hyperparameters while ignoring the fact they are intrinsically related and should be considered together. This motivates us to consider a joint hyperparameter and architecture search method to design CF models. However, this is not easy because of the large search space and high evaluation cost. To solve these challenges, we reduce the space by screening out usefulness yperparameter choices through a comprehensive understanding of individual hyperparameters. Next, we propose a two-stage search algorithm to find proper configurations from the reduced space. In the first stage, we leverage knowledge from subsampled datasets to reduce evaluation costs; in the second stage, we efficiently fine-tune top candidate models on the whole dataset. Extensive experiments on real-world datasets show better performance can be achieved compared with both hand-designed and previous searched models. Besides, ablation and case studies demonstrate the effectiveness of our search framework.