利用社交属性增强关系建模以进行社交媒体人气预测

中文摘要 AI 辅助

近期研究强调了检索增强机制在社交媒体人气预测（SMPP）中的关键作用。虽现有框架借助历史帖子提升了SMPP性能，但因忽视用户生成内容（UGC）实例间的相对关系，检索准确率仍低。为此提出关系增强检索增强框架（RE-Rag），将UGC相似度建模为由语义内容和社交属性共同驱动的连续关系。具体包括用语义属性检索器获取语义和社交属性分布对齐的实例，设计关系引导预测器，通过交叉注意力编码检索实例的多模态特征，引入相对关系图引导注意力权重分配，形成关系引导变换器动态调制注意力权重，融合精炼特征与目标实例进行人气预测。在三个公共基准上的实验表明，RE-Rag在预测准确率和检索效率上均优于现有方法。

英文摘要

Recent studies highlight the critical role of retrieval-augmented mechanisms in social media popularity prediction (SMPP). Although such frameworks have improved SMPP performance by leveraging historical posts, existing methods still suffer from the low retrieval accuracy due to the oversight of relative relationships among UGC instances. To address this limitation, we propose a novel Relation-Enhanced Retrieval-Augmented framework (RE-Rag) that models UGC similarity as a continuous relation jointly driven by semantic content and social attributes. Specifically, RE-Rag employs a Semantic-Attribute Retriever (SAR) to obtain instances aligned in both semantic and social-attribute distributions. Subsequently, we design a Relation-Guided Predictor (RGP): first, cross-attention encodes multimodal features of retrieved instances; then, a relative relation graph is introduced to guide attention weight allocation, forming a Relation-Guided Transformer (RGTs) that dynamically modulate attention weights based on relative attribute relations to capture the interplay between semantics and various social attributes. The refined features are fused with the target instance for popularity prediction. Experiments on three public benchmarks show that RE-Rag consistently outperforms state-of-the-art methods in both prediction accuracy and retrieval efficiency.

发表机构

• Hangzhou Dianzi University（杭州电子科技大学）
• Tianjin University（天津大学）
• City University of Hong Kong（香港城市大学）
• Xinhua Zhiyun Technology Co., Ltd.（新华智云科技有限公司）

机构由 AI 辅助整理，请以论文原文为准。