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 …

As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects
user experience and satisfaction by rearranging the input ranking lists, and thereby plays a …

A dozen recommendation libraries have recently been developed to accommodate popular
recommendation algorithms for reproducibility. However, they are almost simply a collection …

We primarily focus on the field of large language models (LLMs) for recommendation, which
has been actively explored recently and poses a significant challenge in effectively …

Traditional click-through rate (CTR) prediction models convert the tabular data into one-hot
vectors and leverage the collaborative relations among features for inferring the user's …

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 …

The task of related work generation aims to generate a comprehensive survey of related
research topics automatically, saving time and effort for authors. Existing methods simplify …

Recommender systems (RS) are vital for managing information overload and delivering
personalized content, responding to users' diverse information needs. The emergence of …

Industrial multimedia recommendation systems extensively utilize cascade architectures to
deliver personalized content for users, generally consisting of multiple stages like retrieval …

This paper is concerned with TV recommendation, where one major challenge is the
coupling behavior issue that the behaviors of multiple users are coupled together and not …