Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

Anonymization techniques are widely used to make personal data broadly available for
analytics/data-mining purposes while preserving the privacy of the personal information …

Many multimodal recommender systems have been proposed to exploit the rich side
information associated with users or items (eg, user reviews and item images) for learning …

The advent of artificial intelligence-generated content (AIGC) represents a pivotal moment in
the evolution of information technology. With AIGC, it can be effortless to generate high …

The provision of privacy-preserving recommendations for geological tourist attractions is an
important research area. The historical check-in data collected from location-based social …

Privacy-preserving cross-domain recommendation (PPCDR) refers to preserving the privacy
of users when transferring the knowledge from source domain to target domain for better …

Applications that could benefit from automatic understanding of human-human
conversations often come with challenges associated with private information in real-world …

The booming development and huge market of micro-videos bring new e-commerce
channels for merchants. Currently, more micro-video publishers prefer to embed relevant …

The sequential recommender (SR) system is a crucial component of modern recommender
systems, as it aims to capture the evolving preferences of users. Significant efforts have …

A common practice to keep the freshness of an offline Recommender System (RS) is to train
models that fit the user's most recent behaviour while directly replacing the outdated …