A/B testing, also referred to as online controlled experimentation or continuous
experimentation, is a form of hypothesis testing where two variants of a piece of software are …

AI-driven decision-making systems are becoming instrumental in the public sector, with
applications spanning areas like criminal justice, social welfare, financial fraud detection …

Offering incentives (eg, coupons at Amazon, discounts at Uber and video bonuses at Tiktok)
to user is a common strategy used by online platforms to increase user engagement and …

Recommender systems are vulnerable to injective attacks, which inject limited fake users
into the platforms to manipulate the exposure of target items to all users. In this work, we …

Online platforms often incentivize consumers to improve user engagement and platform
revenue. Since different consumers might respond differently to incentives, individual-level …

In modern online platforms, incentives (eg, discounts, bonus) are essential factors that
enhance user engagement and increase platform revenue. Over recent years, uplift …

Real-world recommender systems usually consist of two phases. Predictive models in
Phase I provide accurate predictions of users' actions on items, and Phase II is to aggregate …

The effectiveness of advertising in e-commerce largely depends on the ability of merchants
to bid on and win impressions for their targeted users. The bidding procedure is highly …

Efficiently allocating treatments with a budget constraint constitutes an important challenge
across various domains. In marketing, for example, the use of promotions to target potential …

Black-box heterogeneous treatment effect (HTE) models are increasingly being used to
create personalized policies that assign individuals to their optimal treatments. However …