most citedBridging the Information Gap Between Domain-Specific Model and General LLM for Personalized Recommendation

2 citations · 4 across the 2 of their papers we have counts for

collaborators

3 papers

stat.ML2024

Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection

Zhijin Dong, Hongzhi Liu, Boyuan Ren +2

Anomaly detection is a crucial task in various domains. Most of the existing methods assume the normal sample data clusters around a single central prototype while the real data ma…

cs.AI2024★ 2 cited

Large Language Model with Graph Convolution for Recommendation

Yingpeng Du, Ziyan Wang, Zhu Sun +6

In recent years, efforts have been made to use text information for better user profiling and item characterization in recommendations. However, text information can sometimes be o…

cs.IR2023★ 2 cited

Bridging the Information Gap Between Domain-Specific Model and General LLM for Personalized Recommendation

Wenxuan Zhang, Hongzhi Liu, Yingpeng Du +4

Generative large language models(LLMs) are proficient in solving general problems but often struggle to handle domain-specific tasks. This is because most of domain-specific tasks,…