Artificial Intelligence

    

TransCDR: User Group Enhanced Cross-Domain Recommendation via Transformers

Authors: Cheng Zhang

Cross-domain recommendation (CDR) has become a research hot spot in recent years. CDR learns the information in the source domain and transfer it into the target domain. Recently, autoencoder in deep learning has been utilized in CDR. However, existing method cannot reveal the semantic relationships of latent representations. In this paper, we propose a novel user group enhanced model for CDR based on Transformer (TransCDR) that provides a solution to this challenge. Specifically, we propose a novel user group enhanced methodology and a novel encoder-decoder framework that learns the semantic information via Transformer in the encoded latent space, which open a new research direction for CDR. Experimental results show that our model is competitive with state-of-art methods and can learn the semantic relationships of user rating patterns.

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[v1] 2025-12-05 21:47:38

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