RankElastor mitigates embedding collapse via spectrum-robust token mixing and GLU-based P-FFNs, yielding better performance and scaling on industrial recommendation datasets.
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2026 4verdicts
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MTServe achieves up to 3.1x speedup for generative recommendation model serving by using hierarchical caches with host RAM and system optimizations while keeping cache hit ratios above 98.5%.
Long-Term Embeddings anchor sequential recommendation models to fixed content-based item representations to capture stable preferences and ensure version compatibility, resulting in uplifts in user engagement and financial metrics.
SIGMA deploys a semantic-grounded, instruction-driven generative model with hybrid tokenization and adaptive fusion for multi-task recommendation at AliExpress.
citing papers explorer
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Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation
RankElastor mitigates embedding collapse via spectrum-robust token mixing and GLU-based P-FFNs, yielding better performance and scaling on industrial recommendation datasets.
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MTServe: Efficient Serving for Generative Recommendation Models with Hierarchical Caches
MTServe achieves up to 3.1x speedup for generative recommendation model serving by using hierarchical caches with host RAM and system optimizations while keeping cache hit ratios above 98.5%.
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Long-Term Embeddings for Balanced Personalization
Long-Term Embeddings anchor sequential recommendation models to fixed content-based item representations to capture stable preferences and ensure version compatibility, resulting in uplifts in user engagement and financial metrics.
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SIGMA: A Semantic-Grounded Instruction-Driven Generative Multi-Task Recommender at AliExpress
SIGMA deploys a semantic-grounded, instruction-driven generative model with hybrid tokenization and adaptive fusion for multi-task recommendation at AliExpress.