Personalized news recommendation methods are widely used in online news services. These methods usually recommend news based on the matching between news content and user interest inferred from historical behaviors. However, these methods usually have difficulties in making accurate recommendations to cold-start users, and tend to recommend similar news with those users have read. In general, popular news usually contain important information and can attract users with different interests. Besides, they are usually diverse in content and topic. Thus, in this paper we propose to incorporate news popularity information to alleviate the cold-start and diversity problems for personalized news recommendation. In our method, the ranking score for recommending a candidate news to a target user is the combination of a personalized matching score and a news popularity score. The former is used to capture the personalized user interest in news. The latter is used to measure time-aware popularity of candidate news, which is predicted based on news content, recency, and real-time CTR using a unified framework. Besides, we propose a popularity-aware user encoder to eliminate the popularity bias in user behaviors for accurate interest modeling. Experiments on two real-world datasets show our method can effectively improve the accuracy and diversity for news recommendation.
翻译：个人化新闻建议方法在网上新闻服务中广泛使用。这些方法通常根据新闻内容和用户对历史行为的兴趣之间的匹配来推荐新闻。但是,这些方法通常难以向冷却的用户提供准确的建议,而且往往向这些用户推荐类似的新闻。一般而言,流行新闻通常包含重要信息,能够吸引不同兴趣的用户。此外,它们的内容和主题通常各不相同。因此,我们提议在本文中加入新闻普及信息,以缓解个人化新闻建议中的冷感和多样性问题。在我们的方法中,向目标用户推荐候选人新闻的排名是个人化的匹配分数和新闻普及度分的组合。在两个真实世界的数据集上进行实验,以了解个人化用户对新闻的兴趣。后者用来衡量候选人新闻在时间上的受欢迎程度,根据新闻内容、真实性、真实性和实时CTR预测,使用一个统一的框架。此外,我们提议使用一个广受欢迎的用户编码器来消除用户行为在准确的利息模型方面的受欢迎性偏差。两个真实世界数据集的实验显示我们的方法可以有效地改进新闻建议的准确性和多样性建议。
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