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arXiv · 2601.20199

MERGE: Next-Generation Item Indexing Paradigm for Large-Scale Streaming Recommendation

Jing Yan·Yimeng Bai·Zongyu Liu·Yahui Liu·Junwei Wang·Jingze Huang·Haoda Li·Sihao Ding·Shaohui Ruan·Yang Zhang
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Abstract

Item indexing, which maps a large corpus of items into compact discrete representations, is critical for both discriminative and generative recommender systems, yet existing Vector Quantization (VQ)-based approaches struggle with the highly skewed and non-stationary item distributions common in industrial streaming recommenders, leading to poor assignment accuracy, imbalanced cluster occupancy, and insufficient cluster separation. To address these challenges, we propose MERGE, a next-generation item indexing paradigm that adaptively constructs clusters from scratch, dynamically monitors cluster occupancy, and forms hierarchical index structures via fine-to-coarse merging. Extensive experiments demonstrate that MERGE significantly improves assignment accuracy, cluster uniformity, and cluster separation compared with existing indexing methods, while online A/B tests show substantial gains in key business metrics, highlighting its potential as a foundational indexing approach for large-scale recommendation. Codes are available at https://github.com/baiyimeng/MERGE.

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Jing Yan, Yimeng Bai, Zongyu Liu, Yahui Liu, Junwei Wang, Jingze Huang, Haoda Li, Sihao Ding, Shaohui Ruan, Yang Zhang. 2026-01-28. MERGE: Next-Generation Item Indexing Paradigm for Large-Scale Streaming Recommendation. https://doi.org/10.1145/3799682.3840083

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