As information filtering services, recommender systems have extremely enriched our daily
life by providing personalized suggestions and facilitating people in decision-making, which …

Feed recommendation systems, which recommend a sequence of items for users to browse
and interact with, have gained significant popularity in practical applications. In feed …

Device Model Generalization (DMG) is a practical yet under-investigated research topic for
on-device machine learning applications. It aims to improve the generalization ability of pre …

Modern online platforms are increasingly employing recommendation systems to address
information overload and improve user engagement. There is an evolving paradigm in this …

Existing video-audio understanding models are trained and evaluated in an intra-domain
setting, facing performance degeneration in real-world applications where multiple domains …

In recommender systems, users' behavior data are driven by the interactions of user-item
latent factors. To improve recommendation effectiveness and robustness, recent advances …

Due to the continuously improving capabilities of mobile edges, recommender systems start
to deploy models on edges to alleviate network congestion caused by frequent mobile …

With the explosive growth of online information, recommendation systems have become
essential tools for alleviating information overload. In recent years, researchers have …

Abstract Conventional Domain Generalization (CDG) utilizes multiple labeled source
datasets to train a generalizable model for unseen target domains. However, due to …

On-device machine learning enables the lightweight deployment of recommendation
models in local clients, which reduces the burden of the cloud-based recommenders and …