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 this https URL.
From: Yimeng Bai [view email]
[v1]
Wed, 28 Jan 2026 02:56:30 UTC (1,056 KB)
[v2]
Tue, 11 Aug 2026 03:15:39 UTC (1,051 KB)
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