@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Intention Knowledge Graph Construction for User Intention Relation Modeling
%A Bai, Jiaxin
%A Wang, Zhaobo
%A Cheng, Junfei
%A Yu, Dan
%A Huang, Zerui
%A Wang, Weiqi
%A Liu, Xin
%A Luo, Chen
%A Zhu, Yanming
%A Li, Bo
%A Song, Yangqiu
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F bai-etal-2026-intention
%X 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.
%U https://aclanthology.org/2026.eacl-long.21/
%P 466-484
Markdown (Informal)
[Intention Knowledge Graph Construction for User Intention Relation Modeling](https://aclanthology.org/2026.eacl-long.21/) (Bai et al., EACL 2026)
ACL
- Jiaxin Bai, Zhaobo Wang, Junfei Cheng, Dan Yu, Zerui Huang, Weiqi Wang, Xin Liu, Chen Luo, Yanming Zhu, Bo Li, and Yangqiu Song. 2026. Intention Knowledge Graph Construction for User Intention Relation Modeling. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 466–484, Rabat, Morocco. Association for Computational Linguistics.