This paper summarizes the display advertising literature, organizing the content by the
agents in the display advertising ecosystem, and proposes new research directions. In doing …

Predicting user responses, such as clicks and conversions, is of great importance and has
found its usage inmany Web applications including recommender systems, websearch and …

Predicting user responses, such as click-through rate and conversion rate, are critical in
many web applications including web search, personalised recommendation, and online …

Promising federated learning coupled with Mobile Edge Computing (MEC) is considered as
one of the most promising solutions to the AI-driven service provision. Plenty of studies focus …

In this work, we aim to design a data marketplace; a robust real-time matching mechanism to
efficiently buy and sell training data for Machine Learning tasks. While the monetization of …

User response prediction is a crucial component for personalized information retrieval and
filtering scenarios, such as recommender system and web search. The data in user …

The majority of online display ads are served through real-time bidding (RTB)---each ad
display impression is auctioned off in real-time when it is just being generated from a user …

Reinforcement learning algorithms describe how an agent can learn an optimal action policy
in a sequential decision process, through repeated experience. In a given environment, the …

Real-time advertising allows advertisers to bid for each impression for a visiting user. To
optimize specific goals such as maximizing revenue and return on investment (ROI) led by …

In today's digital era, the importance of digital marketing has increased from one year to
another as a way of providing novel properties for informing, engaging, and selling services …