解决直播推荐中的数据稀疏问题，提升跨域用户偏好建模效果。
FARM: Frequency-Aware Model for Cross-Domain Live-Streaming Recommendation
- 用傅里叶变换捕捉用户高频但稀疏的互动行为，增强偏好感知。
- 通过对比学习对齐短视频与直播间的用户偏好，再融合多注意力机制。
- 在快手平台验证，显著提升推荐效果，已服务数亿用户。
直播服务因实时互动与娱乐性广受欢迎，用户通过点赞、评论、送礼等行为表达偏好。然而，直播推荐面临严重数据稀疏问题：一是用户有效行为（如点赞、送礼）本就稀疏，易被模型忽略；二是平台主推内容为短视频，其曝光量是直播的9倍，导致直播内容难以充分建模用户偏好。为此，本文提出频率感知的跨域直播推荐模型FARM。首先设计域内频率感知模块，利用离散傅里叶变换（DFT）捕捉用户高频但稀疏的行为信号。为实现短视频与直播间用户偏好的迁移，提出“先对齐后融合”策略：通过对比学习对齐双域偏好，再使用定制化注意力机制融合偏好。在快手直播服务上的大量离线实验与在线A/B测试均证明FARM有效且优越，目前已上线并服务于数亿用户。
原文摘要 · Abstract (English)
Live-streaming services have attracted widespread popularity due to their real-time interactivity and entertainment value. Users can engage with live-streaming authors by participating in live chats, posting likes, or sending virtual gifts to convey their preferences and support. However, the live-streaming services faces serious data-sparsity problem, which can be attributed to the following two points: (1) User's valuable behaviors are usually sparse, e.g., like, comment and gift, which are easily overlooked by the model, making it difficult to describe user's personalized preference. (2) The main exposure content on our platform is short-video, which is 9 times higher than the exposed live-streaming, leading to the inability of live-streaming content to fully model user preference. To this end, we propose a Frequency-Aware Model for Cross-Domain Live-Streaming Recommendation, termed as FARM. Specifically, we first present the intra-domain frequency aware module to enable our model to perceive user's sparse yet valuable behaviors, i.e., high-frequency information, supported by the Discrete Fourier Transform (DFT). To transfer user preference across the short-video and live-streaming domains, we propose a novel preference align before fuse strategy, which consists of two parts: the cross-domain preference align module to align user preference in both domains with contrastive learning, and the cross-domain preference fuse module to further fuse user preference in both domains using a serious of tailor-designed attention mechanisms. Extensive offline experiments and online A/B testing on Kuaishou live-streaming services demonstrate the effectiveness and superiority of FARM. Our FARM has been deployed in online live-streaming services and currently serves hundreds of millions of users on Kuaishou.
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