Search Results for author:
Found 12 papers, 1 papers with code
LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation
As personalized recommendation systems become vital in the age of information overload, traditional methods relying solely on historical user interactions often fail to fully capture the multifaceted nature of human interests.
Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction
To address this, we propose a Task-Driven Causal Feature Distillation model (TDCFD) to transform original feature values into causal feature attributions for the specific risk prediction task.
Data-Centric Financial Large Language Models
Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance.
Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference
Asynchronous time series, also known as temporal event sequences, are the basis of many applications throughout different industries.
Enhancing Recommender Systems with Large Language Model Reasoning Graphs
In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs.
Leveraging Large Language Models for Pre-trained Recommender Systems
In this paper, we propose RecSysLLM, a novel pre-trained recommendation model based on LLMs.
Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift
From the perspective of invariant learning and stable learning, a recently well-established paradigm for out-of-distribution generalization, stable features of the graph are assumed to causally determine labels, while environmental features tend to be unstable and can lead to the two primary types of distribution shifts.
Robust Direct Learning for Causal Data Fusion
In the era of big data, the explosive growth of multi-source heterogeneous data offers many exciting challenges and opportunities for improving the inference of conditional average treatment effects.
Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction
In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest in the recent literature.
Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems
Existing recommendation algorithms mostly focus on optimizing traditional recommendation measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-$k$ recommendation lists in terms of precision, recall, MAP, etc.
KNET: A General Framework for Learning Word Embedding using Morphological Knowledge
In particular, we introduce a novel neural network architecture called KNET that leverages both contextual information and morphological word similarity built based on morphological knowledge to learn word embeddings.
