26 citations · 104 across the 32 of their papers we have counts for
7 papers · 1 filter
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation
Mingjia Yin, Hao Wang, Xiang Xu +7
The sequential recommendation system has been widely studied for its promising effectiveness in capturing dynamic preferences buried in users' sequential behaviors. Despite the con…
KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification
Likang Wu, Junji Jiang, Hongke Zhao +4
Recently, Zero-Shot Node Classification (ZNC) has been an emerging and crucial task in graph data analysis. This task aims to predict nodes from unseen classes which are unobserved…
Generative Job Recommendations with Large Language Model
Zhi Zheng, Zhaopeng Qiu, Xiao Hu +3
The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process. Predominantly, current job recomme…
Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations
Likang Wu, Zhaopeng Qiu, Zhi Zheng +2
Large Language Models (LLMs) have revolutionized natural language processing tasks, demonstrating their exceptional capabilities in various domains. However, their potential for be…
Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation
Likang Wu, Zhi Li, Hongke Zhao +5
Zero-Shot Learning (ZSL), which aims at automatically recognizing unseen objects, is a promising learning paradigm to understand new real-world knowledge for machines continuously.…
A Survey on Large Language Models for Recommendation
Likang Wu, Zhi Zheng, Zhaopeng Qiu +9
Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recomm…
GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation
Yongqiang Han, Likang Wu, Hao Wang +5
Sequential Recommendation is a widely studied paradigm for learning users' dynamic interests from historical interactions for predicting the next potential item. Although lots of r…