Dr Xi Wang

About

Xi Wang is a Lecturer of Natural Language Processing at the University of Sheffield. Previously, he obtained his PhD from the University of Glasgow, supervised by Prof. Iadh Ounis and Prof. Craig Macdonald and focused on the research topic of developing review text-based recommender systems. After that, he joined the web intelligence group of University College London (UCL) as a postdoctoral researcher, supervised by Prof. Emine Yilmaz and worked on knowledge-augmented task-oriented dialogue systems. His current research interests are conversational AI, personalisation, and retrieval augmented generation, fairness, as well as relevant topics in the domain of natural language processing (NLP).

Selected publications

• Cao M, Tan X, Akhter ME, Valentino M, Liakata M, Wang X & Aletras N (2026) Fundamental Reasoning Paradigms Induce Out-of-Domain Generalization in Language Models. Findings of the Association for Computational Linguistics: ACL 2026, 33025-33046.
• Liu S, Wang X, Macdonald C & Ounis I (2024) A Social-aware Gaussian Pre-trained model for effective cold-start recommendation. Information Processing & Management, 61(2), 103601-103601.
• Wang X, Ounis I & Macdonald C (2022) BanditProp: Bandit Selection of Review Properties for Effective Recommendation. ACM Transactions on the Web, 16(4), 1-19.
• Sen P, Wang X, Xu R & Yilmaz E () Task2KB: A Public Task-Oriented Knowledge Base. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16482-16484.
• Wang X, Wu Y, Wang X, Macdonald C & Ounis I () University of Glasgow Terrier Team at the TREC 2020 Deep Learning Track.
• Su T, Wang X, Macdonald C & Ounis I () University of Glasgow Terrier Team at the TREC 2019 Deep Learning Track.
• Ramos J, Xia F, Wang X, Chatterjee S, Fu X, Rahmani HA & Lipani A (2026) Interplay: Training Independent Simulators for Reference-Free Conversational Recommendation, Lecture Notes in Computer Science (pp. 629-644). Springer Nature Switzerland View this article in WRRO
• Parapar J, Perez A, Wang X & Crestani F (2026) Overview of eRisk 2025: Early Risk Prediction on the Internet, Lecture Notes in Computer Science (pp. 242-265). Springer Nature Switzerland
• Perez A, Parapar J, Wang X & Crestani F (2026) eRisk 2026: Tasks on Symptoms Ranking, Contextual and Conversational Approaches for Early Mental Health Detection, Lecture Notes in Computer Science (pp. 233-241). Springer Nature Switzerland
• Wang X, Fang A, Ounis I & Macdonald C (2019) Evaluating Similarity Metrics for Latent Twitter Topics, Lecture Notes in Computer Science (pp. 787-794). Springer International Publishing