4 citations · 11 across the 15 of their papers we have counts for
5 papers · 1 filter
TF-DCon: Leveraging Large Language Models (LLMs) to Empower Training-Free Dataset Condensation for Content-Based Recommendation
Jiahao Wu, Qijiong Liu, Hengchang Hu +5
Modern techniques in Content-based Recommendation (CBR) leverage item content information to provide personalized services to users, but suffer from resource-intensive training on…
Dataset Condensation for Recommendation
Jiahao Wu, Wenqi Fan, Jingfan Chen +5
Training recommendation models on large datasets requires significant time and resources. It is desired to construct concise yet informative datasets for efficient training. Recent…
Enhancing Graph Collaborative Filtering via Uniformly Co-Clustered Intent Modeling
Jiahao Wu, Wenqi Fan, Shengcai Liu +3
Graph-based collaborative filtering has emerged as a powerful paradigm for delivering personalized recommendations. Despite their demonstrated effectiveness, these methods often ne…
Multi-Domain Learning From Insufficient Annotations
Rui He, Shengcai Liu, Jiahao Wu +2
Multi-domain learning (MDL) refers to simultaneously constructing a model or a set of models on datasets collected from different domains. Conventional approaches emphasize domain-…
FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation
Qijiong Liu, Jieming Zhu, Jiahao Wu +3
User-curated item lists, such as video-based playlists on Youtube and book-based lists on Goodreads, have become prevalent for content sharing on online platforms. Item list contin…