Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-
standing challenge for Graph Neural Networks (GNNs) due to the overwhelming …

Capturing users' precise preferences is of great importance in various recommender
systems (eg, e-commerce platforms and online advertising sites), which is the basis of how …

E-commerce platforms usually display a mixed list of ads and organic items in feed. One key
problem is to allocate the limited slots in the feed to maximize the overall revenue as well as …

Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-
standing challenge for Graph Neural Networks (GNNs) due to the overwhelming …

A contemporary feed application usually provides blended results of organic items and
sponsored items~(ads) to users. Conventionally, ads are exposed at fixed positions. Such a …

Organic recommendation and advertising recommendation usually coexist on e-commerce
platforms. In this paper, we study the problem of utilizing data from organic recommendation …

Crowdsourcing is a hotspot research field which can facilitate machine learning by collecting
labels to train models. Consequently, the state-of-the-art research efforts in crowdsourcing …

Natural content and advertisement coexist in industrial recommendation systems but differ in
data distribution. Concretely, traffic related to the advertisement is considerably sparser …

Advertising becomes one of the most popular ways of monetizing an online transaction
platform. Usually, sponsored advertisements are posted on the most attractive positions to …

Reinforcement learning (RL) agents need to explore their environments in order to learn
optimal policies by trial and error. However, exploration is challenging when reward signals …