Scalable deep learning-based recommendation systems
We propose a novel collaborative filtering algorithm based on deep neural networks. We use normalized user-rating vector and normalized item-rating vector as inputs to a neural network. The batch normalization technique is used for each layer to prevent neural networks from overfitting. Experimental...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2019-06-01
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| Series: | ICT Express |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405959518302029 |
| Summary: | We propose a novel collaborative filtering algorithm based on deep neural networks. We use normalized user-rating vector and normalized item-rating vector as inputs to a neural network. The batch normalization technique is used for each layer to prevent neural networks from overfitting. Experimental results show that the proposed method outperforms conventional collaborative filtering algorithms. Based on the results, its performance is comparable to the well-known Netflix prize winning algorithm by BellKor. The proposed method has another strong advantage that online operation is possible with little extra complexity and performance degradation. Keywords: Collaborative Filtering, Neural Network |
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| ISSN: | 2405-9595 |