Dan Liu

Shuang Peng, Shuai Zhu, Minghui Yang et al.

With the development of online business, customer service agents gradually play a crucial role as an interface between the companies and their customers. Most companies spend a lot of time and effort on hiring and training customer service agents. To this end, we propose AdaCoach: A Virtual Coach for Training Customer Service Agents, to promote the ability of newly hired service agents before they get to work. AdaCoach is designed to simulate real customers who seek help and actively initiate the dialogue with the customer service agents. Besides, AdaCoach uses an automated dialogue evaluation model to score the performance of the customer agent in the training process, which can provide necessary assistance when the newly hired customer service agent encounters problems. We apply recent NLP technologies to ensure efficient run-time performance in the deployed system. To the best of our knowledge, this is the first system that trains the customer service agent through human-computer interaction. Until now, the system has already supported more than 500,000 simulation training and cultivated over 1000 qualified customer service agents.

Fedor Borisyuk, Mingzhou Zhou, Qingquan Song et al.

We present LiRank, a large-scale ranking framework at LinkedIn that brings to production state-of-the-art modeling architectures and optimization methods. We unveil several modeling improvements, including Residual DCN, which adds attention and residual connections to the famous DCNv2 architecture. We share insights into combining and tuning SOTA architectures to create a unified model, including Dense Gating, Transformers and Residual DCN. We also propose novel techniques for calibration and describe how we productionalized deep learning based explore/exploit methods. To enable effective, production-grade serving of large ranking models, we detail how to train and compress models using quantization and vocabulary compression. We provide details about the deployment setup for large-scale use cases of Feed ranking, Jobs Recommendations, and Ads click-through rate (CTR) prediction. We summarize our learnings from various A/B tests by elucidating the most effective technical approaches. These ideas have contributed to relative metrics improvements across the board at LinkedIn: +0.5% member sessions in the Feed, +1.76% qualified job applications for Jobs search and recommendations, and +4.3% for Ads CTR. We hope this work can provide practical insights and solutions for practitioners interested in leveraging large-scale deep ranking systems.

Yaxing Chen, Qinghua Zheng, Dan Liu et al.

While the security of the cloud remains a concern, a common practice is to encrypt data before outsourcing them for utilization. One key challenging issue is how to efficiently perform queries over the ciphertext. Conventional crypto-based solutions, e.g. partially/fully homomorphic encryption and searchable encryption, suffer from low performance, poor expressiveness and weak compatibility. An alternative method that utilizes hardware-assisted trusted execution environment, i.e., Intel SGX, has emerged recently. On one hand, such work lacks of supporting scalable access control over multiple data users. On the other hand, existing solutions are subjected to the key revocation problem and knowledge extractor vulnerability. In this work, we leverage the newly hardware-assisted methodology and propose a secure, scalable and efficient SQL-like query framework named QShield. Building upon Intel SGX, QShield can guarantee the confidentiality and integrity of sensitive data when being processed on an untrusted cloud platform. Moreover, we present a novel lightweight secret sharing method to enable multi-user access control in QShield, while tackling the key revocation problem. Furthermore, with an additional trust proof mechanism, QShield guarantees the correctness of queries and significantly alleviates the possibility to build a knowledge extractor. We implemented a prototype for QShield and show that QShield incurs minimum performance cost.