An efficient method for autoencoder‐based collaborative filtering

Summary Collaborative filtering (CF) is a widely used technique in recommender systems. With rapid development in deep learning, neural network‐based CF models have gained great attention in the recent years, especially autoencoder‐based CF model. Although autoencoder‐based CF model is faster compar...

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Veröffentlicht in:Concurrency and computation Jg. 31; H. 23
Hauptverfasser: Wang, Yi‐Lei, Tang, Wen‐Zhe, Yang, Xian‐Jun, Wu, Ying‐Jie, Chen, Fu‐Ji
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken Wiley Subscription Services, Inc 10.12.2019
Schlagworte:
Algorithms
autoencoder
Collaboration
collaborative filtering
deep learning
Filtration
Machine learning
Neural networks
recommender system
Recommender systems
Training
ISSN:1532-0626, 1532-0634
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Zusammenfassung:Summary Collaborative filtering (CF) is a widely used technique in recommender systems. With rapid development in deep learning, neural network‐based CF models have gained great attention in the recent years, especially autoencoder‐based CF model. Although autoencoder‐based CF model is faster compared with some existing neural network‐based models (eg, Deep Restricted Boltzmann Machine‐based CF), it is still impractical to handle extremely large‐scale data. In this paper, we practically verify that most non‐zero entries of the input matrix are concentrated in a few rows. Considering this sparse characteristic, we propose a new method for training autoencoder‐based CF. We run experiments on two popular datasets MovieLens 1 M and MovieLens 10 M. Experimental results show that our algorithm leads to orders of magnitude speed‐up for training (stacked) autoencoder‐based CF model while achieving comparable performance compared with existing state‐of‐the‐art models.
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ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.4507