An approach for recommending relevant articles in news portal based on Doc2Vec

2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2022-09-01 DOI:10.1109/AIKE55402.2022.00010

Bogdan Walek, Patrik Müller

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Abstract

News portals are among the most popular websites, and their main goal is to bring the latest news to their readers. Also, it is important to provide relevant content to various types of readers. In this article, we propose an approach for recommending relevant articles on the news portal based on the content of a specific article. The proposed approach is based on Doc2Vec. The main steps of the proposed approach and training of the Doc2Vec model are described. The article also deals with text similarity problems and limitations of the Czech language in the context of recommending relevant articles. For experiment verification of our approach, random articles from the selected news portal were selected. For each article, our approach recommends the most relevant similar articles. Then, the relevant and irrelevant articles were marked. And finally, the ratio of proposed relevant articles for each random article was calculated. The experimental results show the accuracy and relevancy of the proposed approach.

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基于Doc2Vec的新闻门户网站相关文章推荐方法

新闻门户网站是最受欢迎的网站之一，它们的主要目标是向读者提供最新的新闻。此外，为不同类型的读者提供相关的内容也很重要。在本文中，我们提出了一种基于特定文章内容在新闻门户网站上推荐相关文章的方法。提出的方法基于Doc2Vec。描述了该方法的主要步骤和Doc2Vec模型的训练。文章还讨论了捷克语在推荐相关文章时的文本相似问题和局限性。为了实验验证我们的方法，我们从选定的新闻门户网站中随机选择文章。对于每篇文章，我们的方法会推荐最相关的类似文章。然后，对相关和不相关的文章进行标记。最后，计算每一篇随机文章的建议相关文章的比例。实验结果表明了该方法的准确性和相关性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)

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