PrefixMem boosts LLM recommendation accuracy by 46% with Semantic ID encoder
New encoder treats Semantic IDs like images, boosting retrieval recall by 22%.
A new paper proposes PrefixMem, a lightweight encoder for Semantic IDs (SIDs) in generative recommendation. The authors argue SIDs are a distinct modality requiring dedicated encoding, like vision for images. PrefixMem uses prefix n-gram memory tables to provide structured, prefix-conditioned embeddings. Evaluated on large-scale data from Pinterest, it improves deepest-level SID accuracy by up to 46% relative and full-SID retrieval recall by up to 22% relative at matched training compute. Gains are highest (77%) on hard examples where greedy decoding fails.
- PrefixMem improves deepest-level Semantic ID accuracy by up to 46% relative over baselines.
- Full-SID retrieval recall increases by up to 22% relative at matched training compute.
- Hard examples (where greedy decoding fails) see a 77% relative accuracy boost from the encoder.
Why It Matters
Treating Semantic IDs as a distinct modality could unlock more accurate, efficient generative recommendation for platforms like Pinterest.