Výsledky vyhledávání - "Wu, Zhengwei"

Report

Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation

Autor: Wei, Shaowei, Wu, Zhengwei, Li, Xin, Wu, Qintong, Zhang, Zhiqiang, Zhou, Jun, Gu, Lihong, Gu, Jinjie

Sequential recommendation methods play a pivotal role in modern recommendation systems. A key challenge lies in accurately modeling user preferences in the face of data sparsity. To tackle this challenge, recent methods leverage contrastive learning

Externí odkaz: http://arxiv.org/abs/2404.07219

Zobrazit plný text záznamu

Report

Long-tail Augmented Graph Contrastive Learning for Recommendation

Autor: Zhao, Qian, Wu, Zhengwei, Zhang, Zhiqiang, Zhou, Jun

Graph Convolutional Networks (GCNs) has demonstrated promising results for recommender systems, as they can effectively leverage high-order relationship. However, these methods usually encounter data sparsity issue in real-world scenarios. To address

Externí odkaz: http://arxiv.org/abs/2309.11177

Zobrazit plný text záznamu

Report

Generative Contrastive Graph Learning for Recommendation

Autor: Yang, Yonghui, Wu, Zhengwei, Wu, Le, Zhang, Kun, Hong, Richang, Zhang, Zhiqiang, Zhou, Jun, Wang, Meng

By treating users' interactions as a user-item graph, graph learning models have been widely deployed in Collaborative Filtering(CF) based recommendation. Recently, researchers have introduced Graph Contrastive Learning(GCL) techniques into CF to all

Externí odkaz: http://arxiv.org/abs/2307.05100

Zobrazit plný text záznamu