SPARC routes and compresses item attributes for context-aware generative recommendation
_reachsumit · x · 2026-07-29
- SPARC is a sequence-aware progressive attribute routing and compression framework for generative recommendation.
- The paper argues that industrial recommendation data contains heterogeneous attributes such as category, brand, price, behavior type, and timestamp, and that naïvely expanding or compressing them both has drawbacks.
- SPARC first learns context-aware field representations for each attribute type.
- It then routes original, contextual, and identity representations into multiple slots to preserve complementary information under a fixed capacity.
- Finally, lightweight cross-item interaction compresses each historical item into a single token.
- The authors position the method as an Alibaba-oriented approach to making generative recommenders more efficient and context-sensitive.
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