Recommender Systems (RS) have became a popular research topic and, since 2016, Deep Learning methods and techniques have been increasingly explored in this area. News RS are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. The main contribution of this research was named CHAMELEON, a Deep Learning meta-architecture designed to tackle the specific challenges of news recommendation. It consists of a modular reference architecture which can be instantiated using different neural building blocks. As information about users' past interactions is scarce in the news domain, the user context can be leveraged to deal with the user cold-start problem. Articles' content is also important to tackle the item cold-start problem. Additionally, the temporal decay of items (articles) relevance is very accelerated in the news domain. Furthermore, external breaking events may temporally attract global readership attention, a phenomenon generally known as concept drift in machine learning. All those characteristics are explicitly modeled on this research by a contextual hybrid session-based recommendation approach using Recurrent Neural Networks. The task addressed by this research is session-based news recommendation, i.e., next-click prediction using only information available in the current user session. A method is proposed for a realistic temporal offline evaluation of such task, replaying the stream of user clicks and fresh articles being continuously published in a news portal. Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start problem, when compared to other traditional and state-of-the-art session-based recommendation algorithms.

中文翻译：

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CHAMELEON：新闻推荐系统的深度学习元架构 [Phd. 论文]

推荐系统 (RS) 已成为一个热门的研究课题，自 2016 年以来，深度学习方法和技术在该领域得到了越来越多的探索。News RS 旨在个性化用户体验并帮助他们从大型动态搜索空间中发现相关文章。这项研究的主要贡献被命名为 CHAMELEON，这是一种深度学习元架构，旨在解决新闻推荐的特定挑战。它由一个模块化参考架构组成，可以使用不同的神经构建块进行实例化。由于新闻领域中关于用户过去交互的信息很少，因此可以利用用户上下文来处理用户冷启动问题。文章的内容对于解决项目冷启动问题也很重要。此外，在新闻领域，项目（文章）相关性的时间衰减非常快。此外，外部突发事件可能会暂时吸引全球读者的注意力，这种现象通常被称为机器学习中的概念漂移。所有这些特征都通过使用循环神经网络的基于上下文混合会话的推荐方法明确地模拟了这项研究。本研究的任务是基于会话的新闻推荐，即仅使用当前用户会话中可用的信息进行下一点击预测。提出了一种对此类任务进行现实时间离线评估的方法，重播用户点击流和新闻门户中连续发布的新鲜文章。