Deep learning has emerged as a powerful tool in various domains, revolutionising machine
learning research. However, one persistent challenge is the scarcity of labelled training …

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

Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing
users and items into latent representation space, with their correlative patterns from …

Contrastive learning (CL) recently has spurred a fruitful line of research in the field of
recommendation, since its ability to extract self-supervised signals from the raw data is well …

Users' interactions with items are driven by various intents (eg, preparing for holiday gifts,
shopping for fishing equipment, etc.). However, users' underlying intents are often …

Large language models (LLMs) have achieved impressive zero-shot performance in various
natural language processing (NLP) tasks, demonstrating their capabilities for inference …

Contrastive learning (CL) has recently been demonstrated critical in improving
recommendation performance. The underlying principle of CL-based recommendation …

Recent studies show that graph neural networks (GNNs) are prevalent to model high-order
relationships for collaborative filtering (CF). Towards this research line, graph contrastive …

Current sequential recommender systems are proposed to tackle the dynamic user
preference learning with various neural techniques, such as Transformer and Graph Neural …

Self-supervised contrastive representation learning has proved incredibly successful in the
vision and natural language domains, enabling state-of-the-art performance with orders of …