EdgeRec：手机淘宝边缘推荐系统
EdgeRec: Recommender System on Edge in Mobile Taobao
摘要：
推荐系统（RS）已成为大多数网络规模应用中的关键模块。最近，大多数RS都是基于云到边缘框架的瀑布形式，其中推荐的结果被传输到边缘（例如，用户移动）。尽管有效，但云服务器和边缘之间的网络带宽和延迟可能会导致系统反馈和用户感知的延迟。因此，边缘上的实时计算可以帮助更精确地捕捉用户偏好，从而做出更令人满意的推荐。据我们所知，我们的工作是第一次尝试设计和实现新的边缘推荐系统（EdgeRec），它实现了实时用户感知和实时系统反馈。此外，我们提出了异构用户行为序列建模和上下文感知的重排序与行为注意力网络捕捉用户的不同兴趣，并相应地调整推荐结果。在淘宝网首页提要的离线评价和在线评价上的实验结果证明了EdgeRec的有效性。
英文摘要：
Recommender system (RS) has become a crucial module in most web-scale applications. Recently, most RSs are in the waterfall form based on the cloud-to-edge framework, where recommended results are transmitted to edge (e.g., user mobile) by computing in advance in the cloud server. Despite effectiveness, network bandwidth and latency between cloud server and edge may cause the delay for system feedback and user perception. Hence, real-time computing on edge could help capture user preferences more preciously and thus make more satisfactory recommendations. Our work, to our best knowledge, is the first attempt to design and implement the novel Recommender System on Edge (EdgeRec), which achieves Real-time User Perception and Real-time System Feedback. Moreover, we propose Heterogeneous User Behavior Sequence Modeling and Context-aware Reranking with Behavior Attention Networks to capture user's diverse interests and adjust recommendation results accordingly. Experimental results on both the offline evaluation and online performance in Taobao home-page feeds demonstrate the effectiveness of EdgeRec.
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。
