6 papers
Iterative Semantic Reasoning from Individual to Group Interests for Generative Recommendation with LLMs
Xiaofei Zhu, Jinfei Chen, Feiyang Yuan +1
Recommendation systems aim to learn user interests from historical behaviors and deliver relevant items. Recent methods leverage large language models (LLMs) to construct and integ…
Multi-Granularity Sequence Denoising with Weakly Supervised Signal for Sequential Recommendation
Liang Li, Zhou Yang, Xiaofei Zhu
Sequential recommendation aims to predict the next item based on user interests in historical interaction sequences. Historical interaction sequences often contain irrelevant noisy…
E-ICL: Enhancing Fine-Grained Emotion Recognition through the Lens of Prototype Theory
Zhaochun Ren, Zhou Yang, Chenglong Ye +6
In-context learning (ICL) achieves remarkable performance in various domains such as knowledge acquisition, commonsense reasoning, and semantic understanding. However, its performa…
Fine-Grained Emotion Recognition via In-Context Learning
Zhaochun Ren, Zhou Yang, Chenglong Ye +4
Fine-grained emotion recognition aims to identify the emotional type in queries through reasoning and decision-making processes, playing a crucial role in various systems. Recent m…
Exploring Information Processing in Large Language Models: Insights from Information Bottleneck Theory
Zhou Yang, Zhengyu Qi, Zhaochun Ren +4
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of tasks by understanding input information and predicting corresponding outputs. However,…
CMATH: Cross-Modality Augmented Transformer with Hierarchical Variational Distillation for Multimodal Emotion Recognition in Conversation
Xiaofei Zhu, Jiawei Cheng, Zhou Yang +3
Multimodal emotion recognition in conversation (MER) aims to accurately identify emotions in conversational utterances by integrating multimodal information. Previous methods usual…