Bumblebee: Interleaved Mixed-Layer Building Blocks for Large-Scale Recommendation Systems
Recommendation systems have undergone significant transformations in the past years. The transition from traditional feature interaction modules to generative next-action prediction has pushed the boundaries of personalized content. Developments have largely evolved along two separate tracks. Sequence modeling approaches on the one hand and feature interaction methods on the other. In this paper, we introduce Bumblebee, a recommendation architecture that addresses the lack of interaction between the two directions through an interleaved, stackable block design. Each block implements a micro-pipeline of layers combining sequence personalization, attention-based encoding, and feature crossi...
| Authors | David Bauer, Cancan Zhang, Wenshun Liu et al. |
|---|---|
| Primary category | cs.IR |
| Published | 6 July 2026 |
| Last updated at source | 15 September 2026 |
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