I am a second-year Ph.D student in Data Science and Analytics (DSA) thrust at HKUST, where I am very fortunately supervised by Prof. Sunghun KIM and Prof. Raymond Chi Wing WONG.

I graduated from College of Internet of Things Engineering, Hohai University (河海大学物联网工程学院) with a bachelor’s degree and from the School of Electronic and Computer Engineering, Peking University (北京大学信息工程学院) with a master’s degree, advised by Prof.Yuexian Zou. I also collaborate with Prof.Shoujin Wang from University of Technology Sydney closely.

My research interest includes sequential recommendation, medical NLP and large language models. I have published 15+ papers at the top international AI conferences such as NeurIPS, IJCAI, SIGIR, WWW, CIKM, RecSys, ICASSP.

I am now working on Large Language Models + RecSys/Healthcare. If you are seeking any form of academic collaboration or would like to be an Intern/RA (3-6 months) within our group, please feel free to email me at zhoupalin@gmail.com.

🔥 News

• 2024.01:  🎉 1 paper is accepted by WWW 2024.
• 2023.09:  🎉 Our paper “Is ChatGPT a Good Recommender? A Preliminary Study” is accepted by The 1st Workshop on Recommendation with Generative Models (organized by CIKM 2023).
• 2023.09:  🎉 1 paper is accepted by NeurIPS 2023.
• 2023.09:  🧑‍🔧 serve as the reviewer for AAAI2024, ICASSP2024 and LREC-COLING 2024.
• 2023.08:  🎉 1 paper is accepted by CIKM 2023.
• 2023.06:  🎉 2 papers are accepted by RecSys 2023.
• 2023.04:  🎉 1 papers is accepted by IJCAI 2023.
• 2023.02:  🎉 1 papers is accepted by ICASSP 2023.

📝 Publications

📊 Recommender System

🧠 LLM for RecSys

CIKM 2023 GenRec Workshop

Is ChatGPT a Good Recommender? A Preliminary Study
Junling Liu, Chao Liu, Peilin Zhou, Renjie Lv, Kang Zhou, Yan Zhang.

• First work to utilize ChatGPT as a universal recommender, assessing its capabilities across five tasks.
• Provides valuable insights into the strengths and limitations of ChatGPT in recommendation systems.

• ArXiv LLMRec: Benchmarking Large Language Models on Recommendation Task, Jungling Liu, Chao Liu, Peilin Zhou, et al.

• ArXiv Exploring Recommendation Capabilities of GPT-4V (ision): A Preliminary Case Study, Peilin Zhou, Meng Cao, Youliang Huang, Qichen Ye, et al.

🔁 Sequential Recommendation

SIGIR 2022

Decoupled Side Information Fusion for Sequential Recommendation
Yueqi Xie, Peilin Zhou, Sunghun Kim

• Motivation: The early integration of various types of embeddings limits the expressiveness of attention matrices due to a rank bottleneck and constrains the flexibility of gradients.
• Solution: Move the side information from the input to the attention layer and decouples the attention calculation of various side information and item representation.

CIKM 2023

Attention Calibration for Transformer-based Sequential Recommendation
Peilin Zhou, Qichen Ye, Yueqi Xie, Jingqi Gao, and Sunghun Kim.

• AC-TSR framework can reduce the impact of sub-optimal position encoding and noisy input on the existing transformer-based SRS models with limited overhead.
• Two plug-and-play calibrators, namely spatial calibrator and adversarial calibrator, are designed to rectify the unreliable attention.

RecSys 2023

Equivariant Contrastive Learning for Sequential Recommendation
Peilin Zhou, Jingqi Gao, Yueqi Xie, Qichen Ye, et al.

• Motivation: some augmentation strategies, such as item substitution, can lead to changes in user intent. Learning indiscriminately invariant representations for all augmentation strategies might be sub-optimal.
• Solution: we propose Equivariant Contrastive Learning for Sequential Recommendation (ECL-SR), which endows SR models with great discriminative power, making the learned user behavior representations sensitive to invasive augmentations and insensitive to mild augmentations.

• WWW 2024 Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation, Peilin Zhou, Youliang Huang, Yueqi Xie, et al.

• RecSys 2023 Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems, Yueqi Xie, Jingqi Gao, Peilin Zhou, Qichen Ye, et al.

🏥 AI for Healthcare

📱 Medical Social Media Dataset

NeurIPS 2022

METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets
Peilin Zhou, Zeqiang Wang, Dading Chong, Zhijiang Guo, et al.

• Introduce METS-CoV, the first dataset to include medical entities and targeted sentiments on COVID-19-related tweets.
• METS-CoV fully considers the characteristics of the medical field and can therefore be used to help researchers use natural language processing models to mine valuable medical information from tweets.
• Annotation guidelines, benchmark models, and source code are released for medical social medial research.

• Under Review Streamlining Social Media Information Retrieval for Public Health Research with Deep Learning, Yining Hua, Shixu Lin, Minghui Li, Yujie Zhang, Peilin Zhou, Ying-Chih Lo, Li Zhou, Jie Yang

🧬 Large Languages Models for Healthcare

NeurIPS 2023

Benchmarking Large Language Models on CMExam–A Comprehensive Chinese Medical Exam Dataset
Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, et al.

• We construct CMExam, a dataset sourced from the stringent Chinese National Medical Licensing Examination, featuring 60,000+ multiple-choice questions, with detailed explanations.
• This study aims to spur further exploration of LLMs in medicine by providing a comprehensive benchmark for their evaluation.

Under Review

Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model
Qichen Ye, Junling Liu, Dading Chong, Peilin Zhou, et al.

• Construct the ChiMed dataset, which contains diverse data types (QA, plain texts, knowledge graphs, and dialogues).
• Develop a Chinese medical LLM called Qilin-Med via a multi-stage knowledge injection pipeline.

• Under Review Qilin-Med-VL: Towards Chinese Large Vision-language Model for General Healthcare, Junling Liu, Ziming Wang, Qichen Ye, Dading Chong, Peilin Zhou, Yining Hua

• Under Review A Survey of Large Language Models in Medicine: Progress, Application, and Challenge , Hongjian Zhou, Boyang Gu, Xinyu Zou, Yiru Li, Sam S Chen, Peilin Zhou, Junling Liu, Yining Hua, Chengfeng Mao, Xian Wu, Zheng Li, Fenglin Liu

• Under Review Large Language Models in Mental Health Care: a Scoping Review, Yining Hua, Fenglin Liu, Kailai Yang, Zehan Li, Yi-han Sheu, Peilin Zhou，et al.

🌱 AI for Good

IJCAI 2023

GreenPLM: Cross-lingual pre-trained language models conversion with (almost) no cost
Qingcheng Zeng, Lucas Garay, Peilin Zhou, Dading Chong, et al.

• Propose a simple, heuristic pipeline utilizing bilingual lexicons to translate a source language PLM to a target language PLM with almost no computational cost,significantly reducing carbon emissions for building foundation PLMs in various languages.

🤖 Conventional NLP Tasks

🗣️ Spoken Language Understanding

• ICASSP 2022 Joint Multiple Intent Detection and Slot Filling via Selfdistillation, IEEE International Conference on Acoustics, Speech and Signal Processing, Lisong Chen, Peilin Zhou, Yuexian Zou.
• INTERSPEECH 2022 Calibrate and Refine! A Novel and Agile Framework for ASR-error Robust Intent Detection, Peilin Zhou, Dading Chong, Helin Wang, and Qingcheng Zeng.
• INTERSPEECH 2022 Low-resource Accent Classification in Geographically-proximate Settings: A Forensic and Sociophonetics Perspective, Qingcheng Zeng, Dading Chong, Peilin Zhou, Jie Yang
• ICASSP 2021 Sentiment Injected Iteratively Co-Interactive Network for Spoken Language Understanding, Zhiqi Huang, Fenglin Liu, Peilin Zhou, Yuexian Zou.
• INTERSPEECH 2021 Semantic Transportation Prototypical Network for Few-shot Intent Detection, Weiyuan Xu, Peilin Zhou, Chenyu You, Yuexian Zou.
• ICPR 2020 PIN: A novel parallel interactive network for spoken language understanding, Peilin Zhou, Zhiqi Huang, Fenglin Liu, Yuexian Zou.

📚 Machine Reading Comprehension

• ICASSP 2021 Adaptive bi-directional attention: Exploring multi-granularity representations for machine reading comprehension, Nuo Chen, Fenglin Liu, Chenyu You, Peilin Zhou, Yuexian Zou

🎙️ Audio Pretraining

• ICASSP 2023 Masked Spectrogram Prediction for Self-supervised Audio Pre-training, Dading Chong, Helin Wang, Peilin Zhou, Qingcheng Zeng

🎖 Honors and Awards

• 2023.10 The Third Prize of AI Hackathon (Healthcare Track) by Baichuan AI & Amazon Cloud.
• 2020.10 The Second Prize of Few-shot Spoken Language Understanding Challenge by SMP.
• 2020.10 The Second Prize Scholarship by Peking University.
• 2016.09 Academic Excellence Scholarship by Hohai University.
• 2015.05 National Scholarship by Ministry of Education of the People’s Republic of China.

📖 Educations

• 2022.09 - 2025.07 (Expected), Ph.D. in Data Science Analytics, HKUST.
• 2018.09 - 2021.7, M.Sc. in Computer Applied Technology, Peking University.
• 2013.09 - 2017.7, B.Eng. in Telecommunication Engineering, Hohai University.

🧑‍🔧 Academic Services

• Reviewer (or PC Member): WWW 2024, CVPR 2024, AAAI 2024, NeurIPS 2023/2022, EMNLP 2023, ACL 2023, ICASSP 2024/2023/2021, COLING 2023/2022, Interspeech 2021, Neural Computing and Application

💻 Internships

• 2022.07 - 2022.09, Reseach Assistant, supervised by Sunghun KIM, Hong Kong University of Science and Technology.
• 2021.07 - 2022.08, Reseach Assistant, supervised by Jie Yang, School of Medicine, Zhejiang University.
• 2021.05 - 2022.07, AI Researcher, Upstage.
• 2021.07 - 2022.08, Visiting Student, The Chinese University of Hong Kong (Shenzhen).

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