论文标题
基于混合会话的新闻建议使用经常性神经网络
Hybrid Session-based News Recommendation using Recurrent Neural Networks
论文作者
论文摘要
我们描述了基于会话的新闻建议的混合元结构 - 变色龙 - 能够使用经常性神经网络利用各种信息类型。我们使用一个时间评估协议在两个公共数据集上评估了我们的方法，该协议以现实的方式模拟新闻门户的动态。我们的结果证实了使用RNN和利用有关用户和文章的侧面信息进行建模的会话点击顺序的好处，从而比其他基于会话的算法更高的建议准确性和目录覆盖范围更高。
We describe a hybrid meta-architecture -- the CHAMELEON -- for session-based news recommendation that is able to leverage a variety of information types using Recurrent Neural Networks. We evaluated our approach on two public datasets, using a temporal evaluation protocol that simulates the dynamics of a news portal in a realistic way. Our results confirm the benefits of modeling the sequence of session clicks with RNNs and leveraging side information about users and articles, resulting in significantly higher recommendation accuracy and catalog coverage than other session-based algorithms.
