Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual
improvement among tasks considering their shared knowledge. It is an important topic in …

Multi-task learning has been widely applied in computational vision, natural language
processing and other fields, which has achieved well performance. In recent years, a lot of …

Real-Time Bidding (RTB) has shown remarkable success in display advertising and has
been employed in other advertising scenarios, eg, sponsored search advertising with …

Click-through rate (CTR) prediction is one of the fundamental tasks in the industry,
especially in e-commerce, social media, and streaming media. It directly impacts website …

Since the physical meaning of the fields of the dataset is unknown, we have to use the
feature interaction method to select the correlated features and exclude uncorrelated …

Abstract Auction-based Federated Learning (AFL) is a burgeoning research area. However,
existing bidding strategies for AFL data consumers (DCs) primarily focus on maximizing …

Click-Through Rate (CTR) prediction is a fundamental technique for online advertising
recommendation and the complex online competitive auction process also brings many …

Real-time Bidding (RTB) advertisers wish to know in advance the expected cost and yield of
ad campaigns to avoid trial-and-error expenses. However, Campaign Performance …

Extracting expressive visual features is crucial for accurate Click-Through-Rate (CTR)
prediction in visual search advertising systems. Current commercial systems use off-the …

Auction-based federated learning (AFL) has emerged as an efficient and fair approach to
incentivize data owners (DOs) to contribute to federated model training, garnering extensive …