@inproceedings{bai-etal-2026-intention,
    title = "Intention Knowledge Graph Construction for User Intention Relation Modeling",
    author = "Bai, Jiaxin  and
      Wang, Zhaobo  and
      Cheng, Junfei  and
      Yu, Dan  and
      Huang, Zerui  and
      Wang, Weiqi  and
      Liu, Xin  and
      Luo, Chen  and
      Zhu, Yanming  and
      Li, Bo  and
      Song, Yangqiu",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.21/",
    pages = "466--484",
    ISBN = "979-8-89176-380-7",
    abstract = "Understanding user intentions is challenging for online platforms. Recent work on intention knowledge graphs addresses this but often lacks focus on connecting intentions, which is crucial for modeling user behavior and predicting future actions. This paper introduces a framework to automatically generate an intention knowledge graph, capturing connections between user intentions. Using the Amazon m2 dataset, we construct an intention graph with 351 million edges, demonstrating high plausibility and acceptance. Our model effectively predicts new session intentions and enhances product recommendations, outperforming previous state-of-the-art methods and showcasing the approach{'}s practical utility."
}