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A Speed up Method for Collaborative Filtering with Autoencoders
Wen-Zhe Tang, Yi-Lei Wang, Ying-Jie Wu, Xiao-Dong Wang
321 - 326
10.3233/978-1-61499-722-1-321
Frontiers in Artificial Intelligence and Applications
Volume 293: Fuzzy Systems and Data Mining II
Abstract

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.

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