Multimedia-based recommendation (MMRec) models typically rely on observed user-item
interactions and the multimodal content of items, such as visual images and textual …

With the emergence of social networks, social recommendation has become an essential
technique for personalized services. Recently, graph-based social recommendations have …

Deep learning-based recommender systems have achieved remarkable success in recent
years. However, these methods usually heavily rely on labeled data (ie, user-item …

In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …

Recommendation systems are a critical application of artificial intelligence (AI), driving
personalized user experiences across various platforms. Recent advancements in …

Collaborative Filtering (CF) plays a crucial role in modern recommender systems, leveraging
historical user-item interactions to provide personalized suggestions. However, CF-based …

Collaborative Filtering~(CF) typically suffers from the significant challenge of popularity bias
due to the uneven distribution of items in real-world datasets. This bias leads to a significant …

Recently, the user-side fairness issue in Collaborative Filtering (CF) algorithms has gained
considerable attention, arguing that results should not discriminate an individual or a sub …

Social recommendation provides an auxiliary social network structure to enhance
recommendation performances. By formulating user-user social network and user-item …

Node classification is an essential problem in graph learning. However, many models
typically obtain unsatisfactory performance when applied to few-shot scenarios. Some …