The widespread use of social media platforms results in the generation of vast amounts of user-generated content, which requires efficient methods for categorization and search. Hashtag recommendation systems have emerged as a crucial tool for automatically suggesting relevant hashtags and improving content discoverability. However, existing static models struggle to adapt to the highly dynamic and real-time nature of social media conversations, where new hashtags emerge and existing ones undergo semantic shifts. To address these challenges, this paper presents H-ADAPTS (Hashtag recommendAtion by Detecting and adAPting to Trend Shifts), a BERT-based hashtag recommendation methodology that can detect and adapt to shifts in the main trends and topics underlying social media conversation. Our approach introduces a trend-aware detection mechanism to identify changes in hashtag usage, triggering efficient model adaptation on a (small) set of recent posts. The framework leverages Apache Storm for real-time stream processing, enabling scalable and fault-tolerant analysis of high-velocity social data. Experimental results on two real-world case studies, including the COVID-19 pandemic and the 2020 US presidential election, demonstrate the ability to maintain high recommendation accuracy by adapting to emerging trends. Our methodology significantly outperforms existing solutions, ensuring timely and relevant hashtag recommendations in dynamic environments.

翻译：社交媒体平台的广泛使用导致海量用户生成内容的产生，这需要高效的分类与检索方法。话题标签推荐系统已成为自动推荐相关标签、提升内容可发现性的关键工具。然而，现有的静态模型难以适应社交媒体对话高度动态且实时的特性——新标签不断涌现，现有标签的语义亦会发生漂移。为应对这些挑战，本文提出H-ADAPTS（基于趋势漂移检测与适应的话题标签推荐），这是一种基于BERT的话题标签推荐方法，能够检测并适应社交媒体对话中主流趋势与主题的演变。我们的方法引入趋势感知检测机制以识别标签使用模式的变化，从而触发基于（少量）近期帖子的高效模型自适应。该框架利用Apache Storm实现实时流处理，支持对高速社交数据进行可扩展且容错的分析。在两个真实案例（包括COVID-19疫情与2020年美国总统大选）上的实验结果表明，本方法能通过适应新兴趋势保持较高的推荐准确率。相较于现有解决方案，我们的方法在动态环境中能显著提升性能，确保话题标签推荐的时效性与相关性。