SIDRec learns semantic IDs end to end, boosting CTR metrics by up to 5.15%
_reachsumit · x · 2026-11-04
Paper 2611.01136 proposes SIDRec, an end-to-end recommendation-aware semantic ID (SID) learning framework addressing semantic and objective mismatches in existing SID methods.
- A self-updating fusion mechanism integrates pretrained content representations with collaborative embeddings for preference-aware semantic modeling
- An MMoE-inspired joint optimization with shared experts and task-specific gates lets recommendation supervision directly guide differentiable quantization
- Across three public benchmarks it outperforms all baselines on AUC and GAUC, with up to 5.15% relative improvement
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