Yupeng Hou

YuLan: An Open-source Large Language Model

2024 · arXiv (Cornell University)

Large language models (LLMs) have become the foundation of many applications, leveraging their extensive capabilities in processing and understanding natural language. While many open-source LLMs have been released with technical reports, the lack of training …

RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

2021

In recent years, there are a large number of recommendation algorithms proposed in the literature, from traditional collaborative filtering to deep learning algorithms. However, the concerns about how to standardize open source implementation of recommendation …

Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning

2022 · Proceedings of the ACM Web Conference 2022

Recently, graph collaborative filtering methods have been proposed as an effective recommendation approach, which can capture users’ preference over items by modeling the user-item interaction graphs. Despite the effectiveness, these methods suffer from data sparsity …

Towards Universal Sequence Representation Learning for Recommender Systems

2022 · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

In order to develop effective sequential recommenders, a series of sequence representation learning (SRL) methods are proposed to model historical user behaviors. Most existing SRL methods rely on explicit item IDs for developing the sequence …

A Survey of Large Language Models

2026 · Frontiers of Computer Science

Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities, …