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[2508.01115] A hierarchy tree data structure for behavior-based user segment representation
Summary
This paper introduces a novel hierarchy tree data structure for behavior-based user segmentation, enhancing recommendation systems by addressing cold-start issues and improving user experience.
Why It Matters
As recommendation systems become increasingly vital for user engagement, this research offers a significant advancement in user segmentation techniques. By integrating behavioral data with user attributes, it aims to enhance the effectiveness of recommendations, particularly for new users, which is crucial for businesses relying on personalized content delivery.
Key Takeaways
- Introduces Behavior-based User Segmentation (BUS) for improved user categorization.
- Utilizes a tree-based structure to enhance recommendation systems' effectiveness.
- Demonstrates significant improvements in ranking quality over traditional methods.
- Incorporates social graph data to mitigate bias and enhance fairness in recommendations.
- Achieved successful deployment in production, serving billions of users daily.
Computer Science > Machine Learning arXiv:2508.01115 (cs) [Submitted on 1 Aug 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:A hierarchy tree data structure for behavior-based user segment representation Authors:Yang Liu, Xuejiao Kang, Sathya Iyer, Idris Malik, Ruixuan Li, Juan Wang, Xinchen Lu, Xiangxue Zhao, Dayong Wang, Menghan Liu, Isaac Liu, Feng Liang, Yinzhe Yu View a PDF of the paper titled A hierarchy tree data structure for behavior-based user segment representation, by Yang Liu and 12 other authors View PDF HTML (experimental) Abstract:User attributes are essential in multiple stages of modern recommendation systems and are particularly important for mitigating the cold-start problem and improving the experience of new or infrequent users. We propose Behavior-based User Segmentation (BUS), a novel tree-based data structure that hierarchically segments the user universe with various users' categorical attributes based on the users' product-specific engagement behaviors. During the BUS tree construction, we use Normalized Discounted Cumulative Gain (NDCG) as the objective function to maximize the behavioral representativeness of marginal users relative to active users in the same segment. The constructed BUS tree undergoes further processing and aggregation across the leaf nodes and internal nodes, allowing the generation of popular social content and behavioral patterns for each node in the tree. To further mitigate bias and improve fairness, we us...