3 citations · 5 across the 2 of their papers we have counts for
3 papers
cs.IR2024★ 3 cited
Sequential Recommendation with Latent Relations based on Large Language Model
Shenghao Yang, Weizhi Ma, Peijie Sun +4
Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions. Traditional sequential recommendation methods r…
cs.IR2024★ 2 cited
Common Sense Enhanced Knowledge-based Recommendation with Large Language Model
Shenghao Yang, Weizhi Ma, Peijie Sun +4
Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performan…
cs.IR2023
Collaborative Word-based Pre-trained Item Representation for Transferable Recommendation
Shenghao Yang, Chenyang Wang, Yankai Liu +7
Item representation learning (IRL) plays an essential role in recommender systems, especially for sequential recommendation. Traditional sequential recommendation models usually ut…