Meta-path guided heterogeneous graph neural networks for news recommendation
News recommendation is an important recommendation scenario,and its effective-ness relies on the thorough exploration of news textual information.In recent years,graph neural networks(GNNs)have gained widespread attention in the field of recommendation due to their powerful ability to mine higher-order information.However,there is limited research on the use of heterogeneous graph neural networks in the field of news recommendation,and existing heterogeneous graph recommendation models also suffer from the problem of information loss.In order to fully exploit the high-level information among news,users,textual topics,entities,and categories in the news recommendation scenario,we propose a meta-path guided neighbors interaction recommendation model(MPNRec)for news recommendation.The MPNRec model builds a heterogeneous graph with more types of nodes and edges fully mine high-level informa-tion and improve the performance of news recommendation.On two public datasets(i.e.,MIND small and Adressa lweek),the MPNRec model can reach at least a 2％to 5％improvement in recommendation accuracy when compared with state-of-the-art methods.
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