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摘要
News recommendation (NR) is critical for helping users to navigate the vast amount of information on online news platforms. However, the key challenges of tackling the cold -start problem, comprehensively modeling user interests and accurately encoding news semantics in NR have not yet been effectively addressed by existing methods. In this paper, we propose to alleviate the cold -start problem by exploiting news popularity. We also argue that the browsing history of a user can be organized as a personalized hypergraph to augment user interest modeling. Furthermore, we suggest enriching the representation of a candidate news article by retrieving its similar news. Our proposed NR method, named Popularity Prediction with Semantic Retrieval (PPSR), features a retrieval -driven popularity predictor that leverages click records to predict news popularity by incorporating semantic retrieval. It also comprises a hypergraph-based user encoder that mines the rich and diverse relatedness among news in a user's browsing history. In addition, it designs a semantic -enhanced news encoder that retrieves and utilizes similar news in the training set to enrich semantic encoding for candidate news. Experiments on real -world datasets show that our PPSR can outperform the state-of-the-art methods in terms of higher recommendation accuracy and can well mitigate both the user -level and news -level cold -start problems.
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