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

Adapters, a plug-in neural network module with some tunable parameters, have emerged as
a parameter-efficient transfer learning technique for adapting pre-trained models to …

The advent of large language models has inspired active and promising research focused
on developing text content-based recommendation models. Meanwhile, although image …

ID-based Recommender Systems (RecSys), where each item is assigned a unique identifier
and subsequently converted into an embedding vector, have dominated the de-signing of …

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

Traditional recommender systems have primarily relied on identity representations (IDs) to
model users and items. Recently, the integration of pre-trained language models (PLMs) has …

Real-world recommendation systems commonly offer diverse content scenarios for users to
interact with. Considering the enormous number of users in industrial platforms, it is …