The recent surge in Large Language Models (LLMs) has garnered significant attention
across numerous fields. Fine-tuning is often required to fit general LLMs for a specific …

Multi-domain recommendation and multi-task recommendation have demonstrated their
effectiveness in leveraging common information from different domains and objectives for …

As the demand for more personalized recommendation grows and a dramatic boom in
commercial scenarios arises, the study on multi-scenario recommendation (MSR) has …

Multi-Domain Recommendation (MDR) has gained significant attention in recent years,
which leverages data from multiple domains to enhance their performance concurrently …

Multimedia online platforms (eg, Amazon, TikTok) have greatly benefited from the
incorporation of multimedia (eg, visual, textual, and acoustic) content into their personal …

Graph contrastive learning (GCL) has emerged as a potent technology for numerous graph
learning tasks. It has been successfully applied to real-world recommender systems, where …

Cross-domain Recommendation (CDR) as one of the effective techniques in alleviating the
data sparsity issues has been widely studied in recent years. However, previous works may …

To enhance the efficacy of multi-scenario services in industrial recommendation systems,
the emergence of multi-domain recommendation has become prominent, which entails …

With the explosive growth of various commercial scenarios, there is an increasing number of
studies on multi-scenario recommendation (MSR) which trains the recommender system …

Multi-scenario recommender systems (MSRSs) have been increasingly used in real-world
industrial platforms for their excellent advantages in mitigating data sparsity and reducing …