  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.
