Papers by Xing Zi
PersonaX: A Recommendation Agent-Oriented User Modeling Framework for Long Behavior Sequence (2025.findings-acl)
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| Challenge: | Existing methods for user profile modeling extract only partial segments from full historical behavior sequence, resulting in incomplete modeling and suboptimal profiling. |
| Approach: | They propose an agent-agnostic LLM-UM framework to augment downstream recommendation agents . it segments complete historical behaviors into clustered groups and performs offline multi-persona profiling . |
| Outcome: | The proposed framework improves agent performance and inference efficiency by 31% and 10% using 30–50% of behavioral data. |
Answering Narrative-Driven Recommendation Queries via a Retrieve–Rank Paradigm and the OCG-Agent (2025.emnlp-main)
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| Challenge: | Existing approaches to generate narrative-driven recommendation are based on large language models (LLMs) but the RAG paradigm is inherently ill-suited for such special queries. |
| Approach: | They propose a novel retrieve-rank paradigm that generatively retrieves structurally adaptive and semantically aligned candidates, ensuring both extensive candidate coverage and high-quality information. |
| Outcome: | The proposed paradigm outperforms the existing paradigm and the existing one under real-world scenarios. |
Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums (P19-1)
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| Challenge: | Existing work on teaching machines to ask questions focused on generating fixed answers. |
| Approach: | They propose a model to generate open-answered questions from real-world news for open discussion . they analyze how language use affects the number of answers . |
| Outcome: | The proposed model generates questions with higher quality than most text generation methods. |