Allen Lin
PhD Student
Department of Computer Science and Engineering
Texas A&M University (TAMU)
al001 'at' tamu.edu
Research interests: I am broadly interested in machine learning, natural language processing, and information retrieval, with a special emphasis on AI-powered user-centered systems. More recently, my work primarily focuses on leveraging Large Language Models and Foundation Models to enhance personalization of ads and recommendations for a more tailored user experience.
About me: I am currently a final year PhD student studying at Texas A&M University, advised by Prof. James Caverlee. Before that, I earned a Master's degree in Computer Science from Duke University and a Bachelor's degree in Computer Science from Ohio State University.
Rsearch Experience
- Meta - Modern Recommendation System Research Scientist Intern Aug 2024 - present Project: New paradigm for efficiently training LLMs for CTR & CVR predictions
- Google - DeepMind Student Researcher Oct 2023 – Jan 2024 | May – Aug 2024 Project: Collabrative-Filtering enahnced LLMs for improving user personaliztion in ads and recommendations
- Amazon - Amazon Store Foundational AI Applied Scientist Intern May - Oct 2023 Project: Multi-modal foundation encoder for learning long-sequences customer representation
Publications
See also my publications at Google Scholar .- Allen Lin, Renqin Cai, Yun He, Hanchao Yu, Jing Qian, Rui Li, Qifan Wang, James Caverlee Towards An Efficient LLM Training Paradigm for CTR Prediction Under Review paper [pdf]
- Weizhi Zhang, Yuanchen Bei, Liangwei Yang, Henry Peng Zou, Peilin Zhou, Aiwei Liu, Yinghui Li, Hao Chen, Jianling Wang, Yu Wang, Feiran Huang, Sheng Zhou, Jiajun Bu, Allen Lin, James Caverlee, Fakhri Karray, Irwin King, Philip S Yu. Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap ACM Computing Surveys paper [pdf]
- Allen Lin, Jianmo Ni,Jianling Wang, Benjamin Coleman, Zhankui He, James Caverlee, Ed H. Chi, Derek Zhiyuan Cheng Simple Fusion of Collaborative Signals Improves LLM Recommendation ACM Web Conference TheWebConf (WWW) 2025 - E-commerce Workshop
- Allen Lin, Jianling Wang, Ziwei Zhu, and James Caverlee Federated Conversational Recommender System 46th European Conference on Information Retrieval ECIR 2024 paper [pdf]
- Jinhao Pan, Ziwei Zhu, Jianling Wang, Allen Lin, and James Caverlee End-to-End Adaptive Local Learning for Alleviating Mainstream Bias in Collaborative Filtering 46th European Conference on Information Retrieval ECIR 2024 paper [pdf]
- Allen Lin, Ziwei Zhu, Jianling Wang, and James Caverlee Enhancing User Personalization in Conversational Recommenders ACM Web Conference TheWebConf (WWW) 2023 paper [pdf]
- Allen Lin, Jianling Wang, Ziwei Zhu, and James Caverlee Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems 31st ACM International Conference on Information and Knowledge Management CIKM 2022 paper [pdf]
- Allen Lin, Ziwei Zhu, Jianling Wang, and James Caverlee Towards Fair Conversational Recommender Systems 16th ACM Conference on Recommender Systems RecSys 2022 - FAccTRec: Workshop on Responsible Recommendation paper [pdf]
- Majid Alfifi, Xiangjue Dong, Timo Feldman, Allen Lin, Karthic Madanagopal, Aditya Pethe, Maria Teleki, Zhuoer Wang, Ziwei Zhu, James Caverlee Howdy Y'all: An Alexa TaskBot Alexa Prize TaskBot Challenge Proceedings 2022 paper [pdf]
Service
- Conference Program Committes: AAAI 2023, AAAI 2024
- Conference External Reviewer: SIGIR 2022, CIKM 2022
- Journal Reviewer: ACM Transactions on Information Systems, ACM Transactions on Intelligent Systems and Technology
Selected Awards
- NSF/CIKM Student Author Travel Award
- National Buckeye Scholarship
- Ohio State University Trustees Scholarship