A Speed up Method for Collaborative Filtering with Autoencoders
Tang Wen-Zhe, Yilei Wang, Yingjie Wu, Wang Xiaodong · Frontiers in artificial intelligence and applications · 2016
Collaborative Filtering (CF) is a widely used technique in Recommender System. With recent development in deep learning, Neural network based CF has gained great attention in recent years, especially auto-encoders. However, the main disadvantage of autoencoder based CF is the problem of the large sparse target. In this paper, we propose a training strategy to tackle this issue, We run experiments on two popular real world datasets MovieLens 1M and MovieLens 10M. Experiments show orders of magnitude speed up while Attaining similar accuracy compare to existing autoencoder based CF method.