Yichao Wang

Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models

2021

Effectively modeling feature interactions is crucial for CTR prediction in industrial recommender systems. The state-of-the-art deep CTR models with parallel structure (e.g., DCN) learn explicit and implicit feature interactions through independent parallel networks. However, these …

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 …