Lingfeng Shi

Ph.D. Student at Texas A&M University

lingfeng_profile_2328_original.jpg

I am a Ph.D. student at Texas A&M University, advised by Prof. James Caverlee. My research focuses on recommender systems and information retrieval, with particular interests in sequential recommendation, efficient adaptation, and language-model-based retrieval.

I am broadly interested in building recommendation and retrieval models that remain effective under practical constraints, including limited user history, constrained memory, and changing user intent.

news

Sep 01, 2026 Our paper Closing the Long-Short View Gap in Sequential Recommendation without Cached History was accepted to CIKM 2026. Paper
Jul 01, 2026 DMRetriever: A Family of Models for Improved Text Retrieval in Disaster Management appeared at ACL 2026. Paper
Aug 17, 2025 Our work on how speech disfluencies affect conversational recommender systems appeared at Interspeech 2025. Paper

selected publications

  1. CIKM
    Closing the Long-Short View Gap in Sequential Recommendation without Cached History
    Lingfeng Shi, Chengkai Huang, Lina Yao, and James Caverlee
    In Proceedings of the 35th ACM International Conference on Information and Knowledge Management, 2026
    arXiv Code
  2. ACL
    DMRetriever: A Family of Models for Improved Text Retrieval in Disaster Management
    Kai Yin, Xiangjue Dong, Chengkai Liu, Allen Lin, Lingfeng Shi, Ali Mostafavi, and James Caverlee
    In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics, 2026
    DOI
  3. WWW
    Quantize Sequential Recommenders Without Private Data
    Lingfeng Shi, Yuang Liu, Jun Wang, and Wei Zhang
    In Proceedings of the ACM Web Conference 2023, 2023
    DOI