With the rapid development of online services and web applications, recommender systems
(RS) have become increasingly indispensable for mitigating information overload and …

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 …

With the widespread application of online advertising systems, click-through rate (CTR)
prediction has received more and more attention and research. The most prominent features …

With large language models (LLMs) achieving remarkable breakthroughs in NLP domains,
LLM-enhanced recommender systems have received much attention and have been …

Retrieval-augmented Generation (RAG) has markedly enhanced the capabilities of Large
Language Models (LLMs) in tackling knowledge-intensive tasks. The increasing demands of …

Click-through rate (CTR) prediction plays as a core function module in various personalized
online services. The traditional ID-based models for CTR prediction take as inputs the one …

Large Language Models (LLMs) have shown impressive performance in various domains,
prompting researchers to explore their potential application in recommendation systems …

Most click models focus on user behaviors towards a single list. However, with the
development of user interface (UI) design, the layout of displayed items on a result page …

Graph neural networks (GNNs) have emerged as state-of-the-art methods to learn from
graph-structured data for recommendation. However, most existing GNN-based …

Sequential recommender systems aims to predict the users' next interaction through user
behavior modeling with various operators like RNNs and attentions. However, existing …