In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with highly sparse …

Recommender Systems (RSs), which generate personalized content, have become a
technological tool with diverse applications for users. While numerous RSs have been …

Social relations are often used to improve recommendation quality when user-item
interaction data is sparse in recommender systems. Most existing social recommendation …

Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-
Encoders (VAEs) are widely utilized to model the generative process of user interactions …

With the prevalence of social media, there has recently been a proliferation of
recommenders that shift their focus from individual modeling to group recommendation …

With the increasing demand of users for personalized social services, social
recommendation (SR) has been an important concern in academia. However, current …

Recommender systems often grapple with noisy implicit feedback. Most studies alleviate the
noise issues from data cleaning perspective such as data resampling and reweighting, but …

With the rapid development of online social recommendation system, substantial methods
have been proposed. Unlike traditional recommendation system, social recommendation …

The rise of online multi-modal sharing platforms like TikTok and YouTube has enabled
personalized recommender systems to incorporate multiple modalities (such as visual …

Recommendation systems make predictions chiefly based on users' historical interaction
data (eg, items previously clicked or purchased). There is a risk of privacy leakage when …