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Dual Autoencoder Network With Swap Reconstruction For Cold-Start Recommendation by Chuyên Mai Tất is a document available to read on EtoBox.

What is Dual Autoencoder Network With Swap Reconstruction For Cold-Start Recommendation about?

The paper presents a Dual Autoencoder Network (DAN) designed to address the cold-start recommendation problem in cross-domain scenarios. Unlike traditional methods that follow a two-stage embedding-and-mapping approach, DAN utilizes an end-to-end framework that incorporates graph convolutional networks to capture high-order collaborative information. Experimental results demonstrate that DAN significantly outperforms existing state-of-the-art methods on real-world datasets.

Author

Chuyên Mai Tất

Language

EN