Contrastive Learning (CL) has achieved impressive performance in self-supervised learning
tasks, showing superior generalization ability. Inspired by the success, adopting CL into …

The learning objective plays a fundamental role to build a recommender system. Most
methods routinely adopt either pointwise (eg, binary cross-entropy) or pairwise (eg, BPR) …

In recommender systems, most graph-based methods focus on positive user feedback, while
overlooking the valuable negative feedback. Integrating both positive and negative feedback …

Human trajectory data produced by daily mobile devices has proven its usefulness in
various substantial fields such as urban planning and epidemic prevention. In terms of the …

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

With the capacity to capture high-order collaborative signals, Graph Neural Networks
(GNNs) have emerged as powerful methods in Recommender Systems (RS). However, their …

Loss functions steer the optimization direction of recommendation models and are critical to
model performance, but have received relatively little attention in recent recommendation …

Implicit feedback (eg, user clicks) is widely used in building recommender systems (RS).
However, the inherent notorious exposure bias significantly affects recommendation …

The temperature parameter plays a profound role during training and/or inference with large
foundation models (LFMs) such as large language models (LLMs) and CLIP models …

Abstract Unsupervised Domain Adaptation (UDA) is a promising strategy for representing
unlabeled data through domain alignment. Nonetheless, a considerable number of whole …