提升视频推荐的语义相关性与用户参与度平衡
Balancing Semantic Relevance and Engagement in Related Video Recommendations
- 多任务学习联合优化点击行为与语义相关性
- 融合文本视觉特征，主题匹配率从51%升至63%
- 用反倾向加权减少热门内容偏见，适合工业级部署
相关视频推荐通常依赖协同过滤（CF）驱动的共参与信号，导致推荐缺乏语义连贯性且存在显著热度偏差。本文提出一种新型多目标检索框架，通过改进标准双塔模型，显式平衡语义相关性与用户参与度。方法包括：(a) 多任务学习（MTL）联合优化共参与与语义相关性，强调主题一致性；(b) 融合多模态内容特征（文本与视觉嵌入）以增强语义理解；(c) 采用离策略校正（OPC）与逆倾向加权有效缓解热度偏差。在工业规模数据及两周线上A/B测试中验证，语义相关性显著提升（主题匹配率从51%增至63%），热门视频推荐占比下降13.8%，核心用户参与度指标提升0.04%。该方法成功实现更优的语义连贯性、均衡的参与度和实际可扩展性。
原文摘要 · Abstract (English)
Related video recommendations commonly use collaborative filtering (CF) driven by co-engagement signals, often resulting in recommendations lacking semantic coherence and exhibiting strong popularity bias. This paper introduces a novel multi-objective retrieval framework, enhancing standard two-tower models to explicitly balance semantic relevance and user engagement. Our approach uniquely combines: (a) multi-task learning (MTL) to jointly optimize co-engagement and semantic relevance, explicitly prioritizing topical coherence; (b) fusion of multimodal content features (textual and visual embeddings) for richer semantic understanding; and (c) off-policy correction (OPC) via inverse propensity weighting to effectively mitigate popularity bias. Evaluation on industrial-scale data and a two-week live A/B test reveals our framework's efficacy. We observed significant improvements in semantic relevance (from 51% to 63% topic match rate), a reduction in popular item distribution (-13.8% popular video recommendations), and a +0.04% improvement in our topline user engagement metric. Our method successfully achieves better semantic coherence, balanced engagement, and practical scalability for real-world deployment.
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