2 papers
cs.CL2024
LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following
Kaize Shi, Xueyao Sun, Dingxian Wang +3
E-commerce authoring entails creating engaging, diverse, and targeted content to enhance preference elicitation and retrieval experience. While Large Language Models (LLMs) have re…
cs.IR2024
Mitigating Hidden Confounding Effects for Causal Recommendation
Xinyuan Zhu, Yang Zhang, Fuli Feng +3
Recommender systems suffer from confounding biases when there exist confounders affecting both item features and user feedback (e.g., like or not). Existing causal recommendation m…