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.
View on arXiv@article{bai2025_2412.11500,
title={ Intention Knowledge Graph Construction for User Intention Relation Modeling },
author={ Jiaxin Bai and Zhaobo Wang and Junfei Cheng and Dan Yu and Zerui Huang and Weiqi Wang and Xin Liu and Chen Luo and Yanming Zhu and Bo Li and Yangqiu Song },
journal={arXiv preprint arXiv:2412.11500},
year={ 2025 }
}