EdgeRec: Recommender System on Edge in Mobile Taobao
arXiv:2005.08416
Abstract
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.
arXiv commentAccepted by CIKM 2020; Fully deployed in homepage Guess U Like scenario of Taobao application; Resulting in 9% lift in IPV and 11% lift in GMV
References in corpus (8)
- Neural Machine Translation by Jointly Learning to Align and Translate
- On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
- Federated Learning for Mobile Keyboard Prediction
- Learning Tree-based Deep Model for Recommender Systems
- Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
- Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
- Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks
- Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search