SOLAR: SVD-Optimized Lifelong Attention for Recommendation
Attention mechanism remains the defining operator in Transformers since it provides expressive global credit assignment, yet its quadratic cost in sequence length N makes long-context modeling expensive and often forces truncation or other heuristics. Linear attention reduces complexity to O(Nd^2) by reordering computation through kernel feature maps, but this reformulation drops the softmax mechanism and shifts the attention score distribution. Lifelong recommendation requires efficient attention as well, for large-scale sequence modeling with user histories and candidate items under tight latency and resource constraints. We introduce SVD-Attention, a novel attention mechanism, and SOLA...
| Authors | Chenghao Zhang, Chao Feng, Yuanhao Pu et al. |
|---|---|
| Primary category | cs.IR |
| Published | 3 March 2026 |
| Last updated at source | 29 September 2026 |
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