Building a multi-stage cascade ranking system is a commonly used solution to balance the
efficiency and effectiveness in modern information retrieval (IR) applications, such as …

Content feed, a type of product that recommends a sequence of items for users to browse
and engage with, has gained tremendous popularity among social media platforms. In this …

Scoring a large number of candidates precisely in several milliseconds is vital for industrial
pre-ranking systems. Existing pre-ranking systems primarily adopt the two-tower model …

Click-through rate (CTR) prediction is crucial in recommendation and online advertising
systems. Existing methods usually model user behaviors, while ignoring the informative …

We see widespread adoption of slate recommender systems, where an ordered item list is
fed to the user based on the user interests and items' content. For each recommendation, the …

Cascading architecture has been widely adopted in large-scale advertising systems to
balance efficiency and effectiveness. In this architecture, the pre-ranking model is expected …

In real-world search, recommendation, and advertising systems, the multi-stage ranking
architecture is commonly adopted. Such architecture usually consists of matching, pre …

In recent years, there has been a growing interest in utilizing reinforcement learning (RL) to
optimize long-term rewards in recommender systems. Since industrial recommender …

Data selection bias has been a long-lasting challenge in the machine learning domain,
especially in multi-stage recommendation systems, where the distribution of labeled items …

In an era dominated by information overload, effective recommender systems are essential
for managing the deluge of data across digital platforms. Multi-stage cascade ranking …