Information Retrieval (IR) systems are crucial tools for users to access information, widely
applied in scenarios like search engines, question answering, and recommendation …

Large language models (LLMs) demonstrate powerful capabilities, but they still face
challenges in practical applications, such as hallucinations, slow knowledge updates, and …

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

Vector quantization, renowned for its unparalleled feature compression capabilities, has
been a prominent topic in signal processing and machine learning research for several …

This paper introduces PAG-a novel optimization and decoding approach that guides
autoregressive generation of document identifiers in generative retrieval models through …

Generative retrieval has recently emerged as a promising approach to sequential
recommendation, framing candidate item retrieval as an autoregressive sequence …

The task of multi-behavioral sequential recommendation (MBSR) has grown in importance
in personalized recommender systems, aiming to incorporate behavior types of interactions …

Abstract 4D printing enables the creation of adaptive and reconfigurable devices by
combining additive manufacturing with smart materials. This integration introduces …

InfoNCE loss is commonly used to train dense retriever in information retrieval tasks. It is
well known that a large batch is essential to stable and effective training with InfoNCE loss …

Traditional recommendation models often rely on unique item identifiers (IDs) to distinguish
between items, which can hinder their ability to effectively leverage item content information …