BlossomRec is a sparse attention mechanism that uses two distinct block-level patterns for long-term and short-term interests, fused by a gated output, to reduce computation in sequential recommendation Transformers.
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PRL-PUTS casts utility-weight tuning as a one-step value-based RL task and uses scalarization-parameter Pareto sweeping at inference time to generate and govern a family of policies, reporting +0.13% lift in successful sessions on Pinterest Homefeed.
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BlossomRec: Block-level Fused Sparse Attention Mechanism for Sequential Recommendations
BlossomRec is a sparse attention mechanism that uses two distinct block-level patterns for long-term and short-term interests, fused by a gated output, to reduce computation in sequential recommendation Transformers.
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A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems
PRL-PUTS casts utility-weight tuning as a one-step value-based RL task and uses scalarization-parameter Pareto sweeping at inference time to generate and govern a family of policies, reporting +0.13% lift in successful sessions on Pinterest Homefeed.