6 papers
ICG: Improving Cover Image Generation via MLLM-based Prompting and Personalized Preference Alignment
Zhipeng Bian, Jieming Zhu, Qijiong Liu +6
Recent advances in multimodal large language models (MLLMs) and diffusion models (DMs) have opened new possibilities for AI-generated content. Yet, personalized cover image generat…
FairFS: Addressing Deep Feature Selection Biases for Recommender System
Xianquan Wang, Zhaocheng Du, Jieming Zhu +3
Large-scale online marketplaces and recommender systems serve as critical technological support for e-commerce development. In industrial recommender systems, features play vital r…
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…
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…
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…
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…