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

• URL: http://arxiv.org/abs/2601.20199v1
• Date: Wed, 28 Jan 2026 02:56:30 GMT
• Title: MERGE: Next-Generation Item Indexing Paradigm for Large-Scale Streaming Recommendation
• Authors: Jing Yan, Yimeng Bai, Zongyu Liu, Yahui Liu, Junwei Wang, Jingze Huang, Haoda Li, Sihao Ding, Shaohui Ruan, Yang Zhang,
• Abstract summary: 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.<n>Extensive experiments demonstrate that MERGE significantly improves assignment accuracy, cluster uniformity, and cluster separation compared with existing indexing methods.<n>Online A/B tests show substantial gains in key business metrics, highlighting its potential as a foundational indexing approach for large-scale recommendation.
• Score: 15.1614576262293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 streaming industry 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.

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