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

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 …

Human behavioral patterns and consumption paradigms have emerged as pivotal
determinants in environmental degradation and climate change, with quotidian decisions …

We present Legommenders, a unique library designed for content-based recommendation
that enables the joint training of content encoders alongside behavior and interaction …

News recommender systems play a critical role in mitigating the information overload
problem. In recent years, due to the successful applications of large language model …

Service recommendation has emerged as a prominent research area in recent years,
attracting considerable attention due to its ability to improve the efficiency and effectiveness …

Recommender systems stand out as a prime example of machine learning's success,
designed to ease the decision-making process for users by automatically suggesting items …