Abstract
Due to the complexity and diversity of real-world relationships, recommender systems are better suited to represent complex data using heterogeneous information networks (HINs), called HIN-based recommendations. However, it is a challenge to efficiently obtain the embedding and remove the noise from the dataset. In our work, we innovatively propose a recommendation model called Heterogeneous Graph Convolutional Network Recommendation with Adaptive Denoising Training (HGCRD). Our model uses a random walk strategy based on meta-path to obtain a valid sequence of nodes. Then for the generated node networks, we use graph convolutional networks (GCNs) to learn the node embeddings. Also, to eliminate the noise in the dataset, we incorporate an adaptive denoising training (ADT) strategy in the training. Experimental results on three public datasets show that HGCRD performs significantly better than the competitive baseline.
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Acknowledgment
This work is supported by a grant from the Natural Science Foundation of China 62072070 and Social and Science Foundation of Liaoning Province (No. L20BTQ008).
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Jin, S., Zhang, Y., Lu, M. (2022). Heterogeneous Adaptive Denoising Networks for Recommendation. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1637. Springer, Singapore. https://doi.org/10.1007/978-981-19-6142-7_3
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DOI: https://doi.org/10.1007/978-981-19-6142-7_3
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