17 citations · 46 across the 21 of their papers we have counts for
21 papers
Evaluating Recabilities of Foundation Models: A Multi-Domain, Multi-Dataset Benchmark
Qijiong Liu, Jieming Zhu, Yingxin Lai +5
Comprehensive evaluation of the recommendation capabilities of existing foundation models across diverse datasets and domains is essential for advancing the development of recommen…
LANID: LLM-assisted New Intent Discovery
Lu Fan, Jiashu Pu, Rongsheng Zhang +1
Task-oriented Dialogue Systems (TODS) often face the challenge of encountering new intents. New Intent Discovery (NID) is a crucial task that aims to identify these novel intents w…
Can LLMs Outshine Conventional Recommenders? A Comparative Evaluation
Qijiong Liu, Jieming Zhu, Lu Fan +5
In recent years, integrating large language models (LLMs) into recommender systems has created new opportunities for improving recommendation quality. However, a comprehensive benc…
MCNet: Monotonic Calibration Networks for Expressive Uncertainty Calibration in Online Advertising
Quanyu Dai, Jiaren Xiao, Zhaocheng Du +4
In online advertising, uncertainty calibration aims to adjust a ranking model's probability predictions to better approximate the true likelihood of an event, e.g., a click or a co…
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,…
Learning Multi-Aspect Item Palette: A Semantic Tokenization Framework for Generative Recommendation
Qijiong Liu, Jieming Zhu, Zhaocheng Du +3
Traditional recommendation models often rely on unique item identifiers (IDs) to distinguish between items, which can hinder their ability to effectively leverage item content info…
Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning
Yuankai Luo, Hongkang Li, Qijiong Liu +2
We present a novel end-to-end framework that generates highly compact (typically 6-15 dimensions), discrete (int4 type), and interpretable node representations, termed node identif…
Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey
Qijiong Liu, Jieming Zhu, Yanting Yang +6
Personalized recommendation serves as a ubiquitous channel for users to discover information tailored to their interests. However, traditional recommendation models primarily rely…
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.…
Benchmarking News Recommendation in the Era of Green AI
Qijiong Liu, Jieming Zhu, Quanyu Dai +1
Over recent years, news recommender systems have gained significant attention in both academia and industry, emphasizing the need for a standardized benchmark to evaluate and compa…
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…