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Found 11 papers, 7 papers with code
Item-side Fairness of Large Language Model-based Recommendation System
Towards this goal, we develop a concise and effective framework called IFairLRS to enhance the item-side fairness of an LRS.
MolTC: Towards Molecular Relational Modeling In Language Models
Molecular Relational Learning (MRL), aiming to understand interactions between molecular pairs, plays a pivotal role in advancing biochemical research.
LLaRA: Large Language-Recommendation Assistant
Treating the "sequential behaviors of users" as a distinct modality beyond texts, we employ a projector to align the traditional recommender's ID embeddings with the LLM's input space.
Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion
Scrutinizing previous studies, we can summarize a common learning-to-classify paradigm -- given a positive item, a recommender model performs negative sampling to add negative items and learns to classify whether the user prefers them or not, based on his/her historical interaction sequence.
Large Language Model Can Interpret Latent Space of Sequential Recommender
Sequential recommendation is to predict the next item of interest for a user, based on her/his interaction history with previous items.
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation
Therefore, the data sparsity issue of reward signals and state transitions is very severe, while it has long been overlooked by existing RL recommenders. Worse still, RL methods learn through the trial-and-error mode, but negative feedback cannot be obtained in implicit feedback recommendation tasks, which aggravates the overestimation problem of offline RL recommender.
A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems
As the focus on Large Language Models (LLMs) in the field of recommendation intensifies, the optimization of LLMs for recommendation purposes (referred to as LLM4Rec) assumes a crucial role in augmenting their effectiveness in providing recommendations.
Digital twin brain: a bridge between biological intelligence and artificial intelligence
In recent years, advances in neuroscience and artificial intelligence have paved the way for unprecedented opportunities for understanding the complexity of the brain and its emulation by computational systems.
A Survey and Experimental Analysis of Distributed Subgraph Matching
We conduct extensive experiments for both unlabelled matching and labelled matching to analyze the performance of distributed subgraph matching under various settings, which is finally summarized as a practical guide.
Databases
Attention-based 3D Convolutional Network for Alzheimer’s Disease Diagnosis and Biomarkers Exploration
Modern advancements in deep learning provide a powerful framework for disease classification based on neuroimaging data.
Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics
Disorders of consciousness are a heterogeneous mixture of different diseases or injuries.
Neurons and Cognition
