解决视频冷启动推荐难题，提升新内容曝光量36%。
SocRipple: A Two-Stage Framework for Cold-Start Video Recommendations
- 分两阶段：先利用创作者社交关系精准投放，再基于用户行为扩散传播。
- 在大型视频平台实验中，冷启动内容分发量提升36%，用户留存率不变。
- 适合社交型内容平台的新内容推荐场景，尤其关注冷启动问题。
主流推荐系统普遍面临冷启动挑战：新内容缺乏交互历史，难以实现个性化分发。传统协同过滤因信号稀疏而表现不佳，纯内容方法又缺少用户相关性。我们提出SocRipple，一种面向社交图谱平台的冷启动视频推荐两阶段检索框架。第一阶段利用创作者的社交关系进行初始精准曝光；第二阶段基于早期互动信号与历史学习到的稳定用户嵌入，通过K近邻（KNN）搜索实现传播扩散。大规模实验表明，在某大型视频平台上的测试中，SocRipple使冷启动内容分发量提升36%，同时维持冷启动内容的用户参与率，有效平衡了新内容曝光与个性化推荐。
原文摘要 · Abstract (English)
Most industry scale recommender systems face critical cold start challenges new items lack interaction history, making it difficult to distribute them in a personalized manner. Standard collaborative filtering models underperform due to sparse engagement signals, while content only approaches lack user specific relevance. We propose SocRipple, a novel two stage retrieval framework tailored for coldstart item distribution in social graph based platforms. Stage 1 leverages the creators social connections for targeted initial exposure. Stage 2 builds on early engagement signals and stable user embeddings learned from historical interactions to "ripple" outwards via K Nearest Neighbor (KNN) search. Large scale experiments on a major video platform show that SocRipple boosts cold start item distribution by +36% while maintaining user engagement rate on cold start items, effectively balancing new item exposure with personalized recommendations.
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