FARM：跨域直播推荐的频率感知模型
FARM: Frequency-Aware Model for Cross-Domain Live-Streaming Recommendation
AI总结：
针对直播推荐数据稀疏问题，提出FARM模型，通过域内频率感知模块捕捉稀疏有价值行为，采用融合前偏好对齐策略迁移跨域偏好，经实验验证有效且已部署服务快手数亿用户。
摘要：
直播服务因其实时交互性和娱乐价值而广受欢迎。用户可通过参与直播聊天、点赞或发送虚拟礼物来表达偏好与支持，与直播创作者互动。然而，直播服务面临严重的数据稀疏问题，原因有二：（1）用户的有价值行为（如点赞、评论、送礼）通常较为稀疏，易被模型忽略，导致难以刻画用户个性化偏好；（2）平台主要曝光内容为短视频，其曝光量是直播的9倍，使得直播内容无法充分建模用户偏好。为此，我们提出跨域直播推荐的频率感知模型FARM。具体而言，我们首先引入域内频率感知模块，借助离散傅里叶变换（DFT）让模型捕捉用户稀疏但有价值的行为（即高频信息）。为实现短视频与直播域间的用户偏好迁移，我们提出新颖的“融合前偏好对齐”策略，包含两部分：跨域偏好对齐模块，通过对比学习对齐两域用户偏好；跨域偏好融合模块，利用一系列定制化注意力机制进一步融合两域用户偏好。在快手直播服务上的大量离线实验和在线A/B测试证明了FARM的有效性与优越性。FARM已部署于在线直播服务，目前为快手数亿用户提供服务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
