Huifeng Guo

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

2017 · arXiv (Cornell University)

Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature …

A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks

2020

Personalized recommender systems are playing an increasingly important role for online consumption platforms. Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize …

Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation

2022 · arXiv (Cornell University)

Learning vectorized embeddings is at the core of various recommender systems for user-item matching. To perform efficient online inference, representation quantization, aiming to embed the latent features by a compact sequence of discrete numbers, recently …

Diffusion Augmentation for Sequential Recommendation

2023

Sequential recommendation (SRS) has become the technical foundation in many applications recently, which aims to recommend the next item based on the user's historical interactions. However, sequential recommendation often faces the problem of data sparsity, …

LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations

2024 · arXiv (Cornell University)

The lack of training data gives rise to the system cold-start problem in recommendation systems, making them struggle to provide effective recommendations. To address this problem, Large Language Models (LLMs) can model recommendation tasks as …

SampleLLM: Optimizing Tabular Data Synthesis in Recommendations

2025

Tabular data synthesis is crucial in machine learning, yet existing general methods-primarily based on statistical or deep learning models-are highly data-dependent and often fall short in recommender systems. This limitation arises from their difficulty in …

From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs

2025 · arXiv (Cornell University)

Memory is the process of encoding, storing, and retrieving information, allowing humans to retain experiences, knowledge, skills, and facts over time, and serving as the foundation for growth and effective interaction with the world. It …

Product-Based Neural Networks for User Response Prediction over Multi-Field Categorical Data

2018 · ACM Transactions on Information Systems

User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search. The data in user response prediction is mostly in a multi-field categorical format and …