Allen Lin
Scholar

Allen Lin

Google Scholar ID: XmjHN1AAAAAJ
Texas A&M University
Recommender SystemsResponsible AI
Citations & Impact
All-time
Citations
104
 
H-index
4
 
i10-index
3
 
Publications
8
 
Co-authors
4
list available
Contact
Resume (English only)
Academic Achievements
  • Paper 'Towards An Efficient LLM Training Paradigm for CTR Prediction' under review.
  • Paper 'Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap' published in ACM Computing Surveys.
  • Paper 'Simple Fusion of Collaborative Signals Improves LLM Recommendation' to be presented at TheWebConf (WWW) 2025 - E-commerce Workshop.
  • Paper 'Federated Conversational Recommender System' published in 46th European Conference on Information Retrieval ECIR 2024.
  • Paper 'End-to-End Adaptive Local Learning for Alleviating Mainstream Bias in Collaborative Filtering' published in 46th European Conference on Information Retrieval ECIR 2024.
  • Paper 'Enhancing User Personalization in Conversational Recommenders' published in ACM Web Conference TheWebConf (WWW) 2023.
  • Paper 'Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems' published in 31st ACM International Conference on Information and Knowledge Management CIKM 2022.
  • Paper 'Towards Fair Conversational Recommender Systems' published in 16th ACM Conference on Recommender Systems RecSys 2022 - FAccTRec: Workshop on Responsible Recommendation.
  • Paper 'Howdy Y'all: An Alexa TaskBot' published in Alexa Prize TaskBot Challenge Proceedings 2022.
Education
  • PhD student at Texas A&M University, Department of Computer Science and Engineering, advised by Prof. James Caverlee; Master's degree in Computer Science from Duke University; Bachelor's degree in Computer Science from Ohio State University.
Background
  • Research interests: machine learning, natural language processing, and information retrieval, with a special emphasis on AI-powered user-centered systems. Recently, his work primarily focuses on leveraging large language models and foundation models to enhance personalization of ads and recommendations.
Miscellany
  • NSF/CIKM Student Author Travel Award; National Buckeye Scholarship; Ohio State University Trustees Scholarship.