SA-RSQ: Sparse Representation Framework for Multi-modal Recommender Systems
_reachsumit · x · 2026-08-25
The paper proposes SA-RSQ (Sparse Activation-based Residual Soft Quantization) to compress multimodal item embeddings. Using Top-K sparse routing and softmax weights, it stores compact tuples to decouple storage from codebook dimensionality, avoiding accuracy loss from hard quantization. Experiments show favorable reconstruction-performance trade-offs at 8-48 bytes/item, with an online A/B test showing a +2.51% CTR lift.
More from Research
- AAAI 2027 review debate: Should empirical papers without code be auto-rejected? — SimpleObvious4048 · 2026-08-25
- Study asks: Are LLM agents time-aware and budget-conscious? — maksym_andr · 2026-08-25
- Revisiting N2DCG: Empirical Reformulation for Carousel Recommendation — _reachsumit · 2026-08-25
- RAG collapse: LLM answers converge when retrieving self-authored content, 79.6% simulations collapse — _reachsumit · 2026-08-25
- Semantic subword tokenization improves generative recommenders by reducing intra-item attention overload — _reachsumit · 2026-08-25
- Spotify study: better reasoning traces can hurt recommender effectiveness — _reachsumit · 2026-08-25