Scaling Articulated Rationales for MLLM-based Recommendation
We presented SARA, an industrial framework that transforms sparse articulated user rationales into scalable recommendation signals. Its data engine curates questionnaire responses into SARA-HQ, providing explicit preference supervision for aligning SARA-7B through SFT and Quality-Refining DPO. This alignment extends rationale generation from $86{,}564$ questionnaire-covered authors to the full $10$M-author space. SARA-Ranker translates the generated positive and negative rationales into features for user--author interaction modeling and negative-feedback history modeling, connecting articulated reasons to production ranking. Evaluation on unseen authors demonstrates that SARA-7B generates...
| Authors | Haoke Xiao, Yueyang Liu, Yuhui Zhang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 15 September 2026 |
| Last updated at source | 21 September 2026 |
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