构建用户意图关联图谱，提升行为预测与推荐效果
Intention Knowledge Graph Construction for User Intention Relation Modeling
- 自动构建意图知识图谱，捕捉用户意图间连接关系
- 基于Amazon m2数据集生成3.51亿条边的图谱，验证有效性
- 显著提升会话意图预测和商品推荐，适合推荐系统研究者
理解用户意图对在线平台而言具有挑战性。现有意图知识图谱研究常忽略意图间的关联，而这对建模用户行为和预测未来动作至关重要。本文提出一种框架，可自动构建意图知识图谱，捕捉用户意图之间的联系。基于Amazon m2数据集，我们构建了一个包含3.51亿条边的意图图谱，其合理性与接受度均较高。该模型在预测新会话意图及提升商品推荐方面表现优异，优于此前最先进的方法，展现出该方法的实用价值。
原文摘要 · Abstract (English)
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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