用随机游走生成多样化新闻推荐，兼顾内容多样性和可解释性。
D-RDW: Diversity-Driven Random Walks for News Recommender Systems
- 基于可定制分布的随机游走，动态调节推荐多样性
- 在情感与政党提及维度上显著提升多样性指标表现
- 计算效率高于现有模型，适合新闻编辑实时干预
本文提出一种轻量级重排序技术D-RDW，通过结合传统随机游走的多样性能力与新闻属性的可定制目标分布，实现社会导向的新闻推荐。该方法使编辑能透明地将价值规范融入推荐过程。实验表明，相比前沿神经模型，D-RDW在考虑文章情感和政党提及的关键多样性指标上表现更优。此外，D-RDW在计算效率上也优于现有方法。
原文摘要 · Abstract (English)
This paper introduces Diversity-Driven RandomWalks (D-RDW), a lightweight algorithm and re-ranking technique that generates diverse news recommendations. D-RDW is a societal recommender, which combines the diversification capabilities of the traditional random walk algorithms with customizable target distributions of news article properties. In doing so, our model provides a transparent approach for editors to incorporate norms and values into the recommendation process. D-RDW shows enhanced performance across key diversity metrics that consider the articles' sentiment and political party mentions when compared to state-of-the-art neural models. Furthermore, D-RDW proves to be more computationally efficient than existing approaches.
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