Many machine intelligence techniques are developed in E-commerce and one of the most essential components is the representation of IDs, including user ID, item ID, product ID, store ID, brand ID, category ID etc. The classical encoding based methods (like one-hot encoding) are inefficient in that it suffers sparsity problems due to its high dimension, and it cannot reflect the relationships among IDs, either homogeneous or heterogeneous ones. In this paper, we propose an embedding based framework to learn and transfer the representation of IDs. As the implicit feedbacks of users, a tremendous amount of item ID sequences can be easily collected from the interactive sessions. By jointly using these informative sequences and the structural connections among IDs, all types of IDs can be embedded into one low-dimensional semantic space. Subsequently, the learned representations are utilized and transferred in four scenarios: (i) measuring the similarity between items, (ii) transferring from seen items to unseen items, (iii) transferring across different domains, (iv) transferring across different tasks. We deploy and evaluate the proposed approach in Hema App and the results validate its effectiveness.

Cloud computing is becoming increasingly popular due to its power in providing high-performance and flexible service capabilities. More and more internet users have accepted this innovative service model and been using various cloud-based services every day. However, these service-using data is quite valuable for marketing purposes, as it can reflect a user's interest and service-using pattern. Therefore, the privacy issues have been brought out. Recently, many studies focus on access control and other traditional security problems in cloud, and little studied on the topic of the private service data publishing. In this paper, we study the private service data publishing problem by representing the data with a hyper graph, which is quite efficient to illustrate complex relationships among users. We first formulate the problem with a popular background knowledge attack model named rank attack, and then provide an anonymization-based method to prevent the released data from such attacks. We also take data utility into consideration by defining specific information loss metrics. The performances of the methods have been validated by two sets of synthetic data.