LIME: Link-based User-item Interaction Modeling with Decoupled XOR Attention for Efficient Test Time Scaling

LIME is a new recommendation system architecture that tackles the high computational cost of transformers by introducing low‑rank link embeddings and a linear attention mechanism called LIME‑XOR. These innovations decouple user and candidate interactions, allowing pre‑computation of attention weights and reducing inference complexity to be nearly independent of candidate set size and linear in user sequence length. Experiments on public and industrial datasets demonstrate that LIME matches transformer performance while achieving a tenfold speedup on large candidate sets or long sequences, and it has been shown to improve user engagement on a major recommendation platform.

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