This paper proposes a novel representation to support computing metrics that
help understanding and improving in real-time a team's behavior during problem
solving in real-life. Even though teams are important in modern activities,
there is little computing aid to improve their activity. The representation
captures the different mental images developed, enhanced, and utilized during
solving. A case study illustrates the representation.

提出了一种支持实时计算度量的新型表示，用于了解和改进团队在现实问题解决过程中的行为，该表示捕捉了解决过程中团队开发、增强和利用的不同心理形象。通过案例研究来说明该表示方法。

提升团队实时问题解决能力的一种新的表达方式

A Novel Representation to Improve Team Problem Solving in Real-Time

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.

提出一种基于边缘计算的推荐系统 (EdgeRec)，通过异构用户行为序列建模和行为注意力网络对推荐结果进行上下文感知的调整，实现实时用户感知和系统反馈，并通过淘宝主页数据的离线评估和在线性能测试证明了其有效性。