Large Language Model (LLM) has transformative potential in various domains, including
recommender systems (RS). There have been a handful of research that focuses on …

In this age where data is abundant, the ability to distill meaningful insights from the sea of
information is essential. Our research addresses the computational and resource …

In the era of data proliferation, efficiently sifting through vast information to extract
meaningful insights has become increasingly crucial. This paper addresses the …

Traditional recommender systems heavily rely on ID features, which often encounter
challenges related to cold-start and generalization. Modeling pre-extracted content features …

Conventional algorithms for training language models (LMs) with human feedback rely on
preferences that are assumed to account for an" average" user, disregarding subjectivity and …

We propose a novel approach for training large language models (LLMs) to adhere to
objectives defined within a latent embedding space. Our method leverages reinforcement …

Graph contrastive learning (GCL) has emerged as a pivotal technique in the domain of
graph representation learning. A crucial aspect of effective GCL is the caliber of generated …

Estimating the subsurface impedance properties is an essential process in seismic
exploration and reservoir characterization. The accuracy and efficiency of impedance …

Large Language Models (LLMs) are increasingly prominent in the recommendation systems
domain. Existing studies usually utilize in-context learning or supervised fine-tuning on task …

The progress made in the field of medicine and the consequent increase in the prospect of
life have contributed to rise people's interest towards a healthier lifestyle. Fitness activity is …