Abstract Large Language Models (LLMs) have emerged as powerful tools in the field of
Natural Language Processing (NLP) and have recently gained significant attention in the …

In the past decades, recommender systems have attracted much attention in both research
and industry communities. Existing recommendation models mainly learn the underlying …

Time series forecasting holds significant importance in many real-world dynamic systems
and has been extensively studied. Unlike natural language process (NLP) and computer …

Abstract Neural Temporal Point Processes (TPPs) are the prevalent paradigm for modeling
continuous-time event sequences, such as user activities on the web and financial …

In this technical survey, the latest advancements in the field of recommender systems are
comprehensively summarized. The objective of this study is to provide an overview of the …

Harnessing Large Language Models (LLMs) for recommendation is rapidly emerging, which
relies on two fundamental steps to bridge the recommendation item space and the language …

Large Language Models (LLMs) have shown impressive performance in natural language
tasks, but their outputs can exhibit undesirable attributes or biases. Existing methods for …

Recently, large language models such as ChatGPT have showcased remarkable abilities in
solving general tasks, demonstrating the potential for applications in recommender systems …

With the rapid development of online services, recommender systems (RS) have become
increasingly indispensable for mitigating information overload. Despite remarkable …

Recently, there has been growing interest in developing the next-generation recommender
systems (RSs) based on pretrained large language models (LLMs). However, the semantic …