Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation
Sequential recommendation has rapidly advanced in click-through rate prediction due to its ability to model dynamic user interests. A key challenge, however, lies in modeling long sequences: users often exhibit significant interest shifts, introducing substantial irrelevant or misleading information. Our empirical analysis corroborates this challenge and uncovers a recurring behavioral pattern in long sequences (\textit{session hopping}): user interests remain stable within short temporal spans (\textit{sessions}) but shift drastically across sessions and may reappear after multiple sessions. To address this challenge, we propose the Mixture of Sequence (MoS) framework, a model-agnostic M...
| Authors | Xiao Lin, Zhicheng Tang, Weilin Cong et al. |
|---|---|
| Primary category | cs.IR |
| Published | 1 March 2026 |
| Last updated at source | 1 March 2026 |
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