动态检测热点变化，实时推荐精准话题标签。
Dynamic hashtag recommendation in social media with trend shift detection and adaptation
- 通过趋势感知机制识别标签使用变化，捕捉社交话题演化。
- 在疫情和大选案例中，推荐准确率显著优于传统方法。
- 适合需要实时响应社交热点的平台或运营人员。
话题标签推荐系统已成为自动建议相关标签、提升内容分类与搜索效率的关键工具。然而，现有静态模型难以适应社交媒体对话的高度动态性，新标签不断涌现，旧标签语义持续变化。为此，本文提出 H-ADAPTS（基于趋势检测与自适应的话题标签推荐），通过趋势感知机制检测标签使用变化所反映的新兴趋势与话题演变，并基于少量近期帖子触发高效模型更新。同时，利用 Apache Storm 框架实现高吞吐社交数据的可扩展、容错分析，支持及时发现趋势变化。在新冠疫情与2020年美国大选两个真实案例研究中，结果表明 H-ADAPTS 能够及时响应新兴趋势，提供更相关的话题标签推荐，性能显著优于现有方案。
原文摘要 · Abstract (English)
Hashtag recommendation systems have emerged as a key tool for automatically suggesting relevant hashtags and enhancing content categorization and search. However, existing static models struggle to adapt to the highly dynamic nature of social media conversations, where new hashtags constantly emerge and existing ones undergo semantic shifts. To address these challenges, this paper introduces H-ADAPTS (Hashtag recommendAtion by Detecting and adAPting to Trend Shifts), a dynamic hashtag recommendation methodology that employs a trend-aware mechanism to detect shifts in hashtag usage-reflecting evolving trends and topics within social media conversations-and triggers efficient model adaptation based on a (small) set of recent posts. Additionally, the Apache Storm framework is leveraged to support scalable and fault-tolerant analysis of high-velocity social data, enabling the timely detection of trend shifts. Experimental results from two real-world case studies, including the COVID-19 pandemic and the 2020 US presidential election, demonstrate the effectiveness of H-ADAPTS in providing timely and relevant hashtag recommendations by adapting to emerging trends, significantly outperforming existing solutions.
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