新闻推荐的深度神经网络

Keunchan Park, Jisoo Lee, Jaeho Choi
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引用次数: 48

摘要

新闻网站的一个基本作用是推荐有趣的文章。新闻推荐的关键挑战是推荐新发表的文章。与其他领域不同,过时的条目在新闻推荐任务中被认为是不相关的。另一个挑战是在培训阶段看不到推荐候选人。在本文中,我们引入深度神经网络模型来克服这些挑战。我们提出了一种改进的基于会话的递归神经网络(RNN)模型,该模型适合于新闻推荐,以及一种基于历史的RNN模型,该模型涵盖了整个用户的过去历史。最后,我们提出了一个卷积神经网络(CNN)模型来捕获用户偏好并个性化推荐结果。在真实新闻数据集上的实验结果表明,我们的模型优于竞争基线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Neural Networks for News Recommendations
A fundamental role of news websites is to recommend articles that are interesting to read. The key challenge of news recommendation is to recommend newly published articles. Unlike other domains, outdated items are considered to be irrelevant in the news recommendation task. Another challenge is that the recommendation candidates are not seen in the training phase. In this paper, we introduce deep neural network models to overcome these challenges. we propose a modified session-based Recurrent Neural Network (RNN) model tailored to news recommendation as well as a history-based RNN model that spans the whole user's past histories. Finally, we propose a Convolutional Neural Network (CNN) model to capture user preferences and to personalize recommendation results. Experimental results on real-world news dataset shows that our model outperforms competitive baselines.
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