A jointly learned hierarchical index with cross-attention and residual quantization scales exact retrieval in foundational recommendation models, deployed at Meta with additional performance from test-time training on index nodes.
Minimizing flops to learn efficient sparse representations.arXiv preprint arXiv:2004.05665,
3 Pith papers cite this work. Polarity classification is still indexing.
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A neural sparse retrieval system with granular subword tokenization (max 3 chars) achieves 91.4% recall@10 on a 6M music document corpus versus 57.7% for trigrams, with improved HCI exploration efficiency and zero added query latency.
Larger 100K vocabularies in SPLADE models, especially those initialized with ESPLADE pretraining, improve retrieval effectiveness after pruning compared to 32K baselines while keeping similar efficiency.
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Efficient Retrieval Scaling with Hierarchical Indexing for Large Scale Recommendation
A jointly learned hierarchical index with cross-attention and residual quantization scales exact retrieval in foundational recommendation models, deployed at Meta with additional performance from test-time training on index nodes.
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Surface-Form Neural Sparse Retrieval: Robust Fuzzy Matching for Industrial Music Search
A neural sparse retrieval system with granular subword tokenization (max 3 chars) achieves 91.4% recall@10 on a 6M music document corpus versus 57.7% for trigrams, with improved HCI exploration efficiency and zero added query latency.
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The Role of Vocabularies in Learning Sparse Representations for Ranking
Larger 100K vocabularies in SPLADE models, especially those initialized with ESPLADE pretraining, improve retrieval effectiveness after pruning compared to 32K baselines while keeping similar efficiency.