Large-scale e-commercial platforms in the real-world usually contain various recommendation scenarios (domains) to meet demands of diverse customer groups. Multi-Domain Recommendation (MDR), which aims to jointly improve recommendations on all domains and easily scales to thousands of domains, has attracted increasing attention from practitioners and researchers. Existing MDR methods usually employ a shared structure and several specific components to respectively leverage reusable features and domain-specific information. However, data distribution differs across domains, making it challenging to develop a general model that can be applied to all circumstances. Additionally, during training, shared parameters often suffer from domain conflict while specific parameters are inclined to overfitting on data sparsity domains. In this paper, we first present a scalable MDR platform served in Taobao that enables to provide services for thousands of domains without specialists involved. To address the problems of MDR methods, we propose a novel model agnostic learning framework, namely MAMDR, for the multi-domain recommendation. Specifically, we first propose a Domain Negotiation (DN) strategy to alleviate the conflict between domains. Then, we develop a Domain Regularization (DR) to improve the generalizability of specific parameters by learning from other domains. We integrate these components into a unified framework and present MAMDR, which can be applied to any model structure to perform multi-domain recommendation. Finally, we present a large-scale implementation of MAMDR in the Taobao application and construct various public MDR benchmark datasets which can be used for following studies. Extensive experiments on both benchmark datasets and industry datasets demonstrate the effectiveness and generalizability of MAMDR.

Business processes in workflows comprise of an ordered sequence of tasks and decisions to accomplish certain business goals. Each decision point requires the input of a decision-maker to distill complex case information and make an optimal decision given their experience, organizational policy, and external contexts. Overlooking some of the essential factors or lack of knowledge can impact the throughput and business outcomes. Therefore, we propose an end-to-end automated decision support system with explanation for business processes. The system uses the proposed process-aware feature engineering methodology that extracts features from process and business data attributes. The system helps a decision-maker to make quick and quality decisions by predicting the decision and providing an explanation of the factors which led to the prediction. We provide offline and online training methods robust to data drift that can also incorporate user feedback. The system also support predictions with live instance data i.e., allow decision-makers to conduct trials on current data instance by modifying its business data attribute values. We evaluate our system on real-world and synthetic datasets and benchmark the performance, achieving an average of 15% improvement over baselines.