Conversion rate (CVR) prediction is essential in recommender systems, facilitating precise matching between recommended items and users’ preferences. However, the sample selection bias (SSB) and data sparsity (DS) issues pose challenges to accurate ...

Forecasting the ocean dynamics is a critical issue for a wide array of climate extremes and environmental crisis. The dynamic variations are traditionally approached by relying on numerical models with all the related physical processes ...

Shared mobility redefines urban transportation, offering economic and environmental benefits by reducing pollution and urban congestion. However, in the post-pandemic era, the shared mobility sector is grappling with a crisis of trust, particularly ...

Portfolio management is the art and science in fiance that concerns continuous reallocation of funds and assets across financial instruments to meet the desired returns to risk profile. Deep reinforcement learning (RL) has gained increasing interest in ...

Faced with the threat of identity leakage during voice data publishing, users are engaged in a privacy-utility dilemma when enjoying the utility of voice services. Existing machine-centric studies employ direct modification or text-based re-synthesis to ...

Financial documents often contain rich domain information, such as named entities, which could be used to indicate the documents' classification categories. Existing classification methods either ignore such contained financial domain information, ...

Object detection is one of the widest studies in computer vision. And there are many useful algorithms for this task. Though most of the studies are effective in the daytime or dark scene, they are not applicable in some special cases that need to ...

Federated learning (FL) enables participants to collaboratively construct a global machine learning model without sharing their local training data to the remote server. In FL systems, the selection of training samples has a significant impact on model ...

Tensor decomposition is one of the most effective techniques for multi-criteria recommendations. However, it suffers from data sparsity when dealing with three-dimensional (3D) user-item-criterion ratings. To alleviate this problem, we propose a generic ...

In the intelligent era of human-computer symbiosis, the use of machine learning method for covert communication confrontation has become a hot topic of network security. The existing covert communication technology focuses on the statistical abnormality ...

Face recognition has great theory research and application value. It is a very complicated problem which often suffers from variations in lighting condition, facial expression, head pose, glasses, background, and so on. This paper realizes an effective ...

Despite 25 years of research in academia, approximate query processing (AQP) has had little industrial adoption. One of the major causes of this slow adoption is the reluctance of traditional vendors to make radical changes to their legacy codebases, ...

Nowadays, the management style of most distribution networks in rural area is supported by the technology of Computer Management System based on relational database. It doesn't meet the requirement of the increasingly complex situation. GIS technology ...