New Paper Adds World-Model Layer to Recommenders to Steer Future User Behavior
_reachsumit · x · 2026-09-28
An arXiv paper introduces UA-TWM, a plug-in world-model interface for trained sequential recommenders that predicts how candidate slates will shape future user behavior, then selects one that steers toward a target while keeping relevance.
Key points:
- Each displayed slate shapes subsequent feedback and user state, beyond item ranking;
- UA-TWM constructs nearby slate actions, estimates their target-relevant consequences, and picks an alternative under utility constraints, falling back to the reference slate when none qualify;
- Two instantiations: logged-replay combining utility and target-gain estimates with calibrated failure-risk prediction, and a closed-loop version updating decisions after observed feedback.
Experiments across twelve sequential backbones on MovieLens-25M and KuaiRand-Pure show consistent gains in Recall@20, NDCG@20, and future-state alignment, with ablations revealing the utility and risk costs of aggressive target pursuit.
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