Abstract

In this paper we propose a novel recommendation approach using past news click data and the Neural Knowledge DNA (NK-DNA). The Neural Knowledge DNA is a novel knowledge representation method designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and computing systems. We examine our approach for news recommendation tasks on the MIND benchmark dataset. By taking advantages of NK-DNA, deep learning, and the SOEKS technologies, our approach can learn from users’ past behaviors to form reading preference of the user, and reuse learned knowledge for improving the recommendation performance.

中文翻译：

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使用神经知识DNA进行智能推荐

摘要

在本文中，我们提出了一种使用过去新闻点击数据和神经知识DNA（NK-DNA）的新颖推荐方法。神经知识DNA是一种新颖的知识表示方法，旨在支持在机器和计算系统之间发现，存储，重用，改进和共享知识。我们检查了MIND基准数据集上用于新闻推荐任务的方法。通过利用NK-DNA，深度学习和SOEKS技术的优势，我们的方法可以从用户的过去行为中学习，以形成用户的阅读偏好，并重用学到的知识来提高推荐性能。