17 citations · 46 across the 21 of their papers we have counts for
11 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…
EasyGen: Easing Multimodal Generation with BiDiffuser and LLMs
Xiangyu Zhao, Bo Liu, Qijiong Liu +2
We present EasyGen, an efficient model designed to enhance multimodal understanding and generation by harnessing the capabilities of diffusion models and large language models (LLM…
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…
Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization
Qijiong Liu, Lu Fan, Jiaren Xiao +2
Category information plays a crucial role in enhancing the quality and personalization of recommender systems. Nevertheless, the availability of item category information is not co…
How Good Are LLMs at Out-of-Distribution Detection?
Bo Liu, Liming Zhan, Zexin Lu +3
Out-of-distribution (OOD) detection plays a vital role in enhancing the reliability of machine learning (ML) models. The emergence of large language models (LLMs) has catalyzed a p…
Neighborhood-based Hard Negative Mining for Sequential Recommendation
Lu Fan, Jiashu Pu, Rongsheng Zhang +1
Negative sampling plays a crucial role in training successful sequential recommendation models. Instead of merely employing random negative sample selection, numerous strategies ha…
Multi-modal Pre-training for Medical Vision-language Understanding and Generation: An Empirical Study with A New Benchmark
Li Xu, Bo Liu, Ameer Hamza Khan +2
With the availability of large-scale, comprehensive, and general-purpose vision-language (VL) datasets such as MSCOCO, vision-language pre-training (VLP) has become an active area…
Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-training
Haode Zhang, Haowen Liang, Liming Zhan +2
We consider the task of few-shot intent detection, which involves training a deep learning model to classify utterances based on their underlying intents using only a small amount…
ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models
Qijiong Liu, Nuo Chen, Tetsuya Sakai +1
Personalized content-based recommender systems have become indispensable tools for users to navigate through the vast amount of content available on platforms like daily news websi…
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…