Abstract
With the swift development of Internet and artificial intelligence, recommender systems have become more and more important as useful information has a risk of submerging in huge amounts of data or smart services always provide a behavior predictor to meet diversified user’s needs. User-session based recommendations are commonly applied in many modern online platforms. Graph Neural Networks (GNNs) have been shown to have a strong ability to address the problem of session-based recommendation with accurate item embedding. However, there are a lot of application scenarios that have already provided user profiles. We propose a model based on Hierarchical GNNs for personalized recommendations, which evolves both item information in sessions and user profiles. The experiments on two industry datasets show the superiority of our model over the state-of-the-art methods.
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The authors gratefully acknowledge the anonymous reviewers for their helpful suggestions.
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Shen, X. et al. (2019). Hierarchical Graph Neural Networks for Personalized Recommendations with User-Session Context. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2019. Lecture Notes in Computer Science(), vol 11910. Springer, Cham. https://doi.org/10.1007/978-3-030-34139-8_34
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DOI: https://doi.org/10.1007/978-3-030-34139-8_34
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