4 citations · 5 across the 6 of their papers we have counts for
6 papers
Legommenders: A Comprehensive Content-Based Recommendation Library with LLM Support
Qijiong Liu, Lu Fan, Xiao-Ming Wu
We present Legommenders, a unique library designed for content-based recommendation that enables the joint training of content encoders alongside behavior and interaction modules,…
Discrete Semantic Tokenization for Deep CTR Prediction
Qijiong Liu, Hengchang Hu, Jiahao Wu +3
Incorporating item content information into click-through rate (CTR) prediction models remains a challenge, especially with the time and space constraints of industrial scenarios.…
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…
Only Encode Once: Making Content-based News Recommender Greener
Qijiong Liu, Jieming Zhu, Quanyu Dai +1
Large pretrained language models (PLM) have become de facto news encoders in modern news recommender systems, due to their strong ability in comprehending textual content. These hu…
Continual Graph Convolutional Network for Text Classification
Tiandeng Wu, Qijiong Liu, Yi Cao +3
Graph convolutional network (GCN) has been successfully applied to capture global non-consecutive and long-distance semantic information for text classification. However, while GCN…
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…