ControlRec: Editable Intent Modeling for Controllable Generative Recommendation

Generative recommender systems directly generate items from behavioral history, but most leave user intent under-specified and difficult to revise. Sequential recommenders primarily infer preferences from past behavior, while conversational recommenders elicit immediate needs but often make limited use of long-term behavioral preferences. We propose ControlRec, a unified framework for generative recommendation with implicit and explicit intent guidance. Without dialogue, it infers personalized intents by softly assigning historical interactions to shared preference prototypes; with dialogue, it updates these intents from natural-language feedback while preserving behavioral personalization. ControlRec comprises three core designs: (1) A semantic alignment adapter bridges natural language and the semantic IDs (SIDs) space, enabling expressed preferences to guide item generation. (2) A multi-intent condition encoder constructs personalized intent conditions from historical interactions and shared preference prototypes, and updates an editable intent state from natural-language feedback when dialogue is available, preserving behavioral personalization. (3) An intent-factorized diffusion decoder guides generation via parallel intent-specific denoising, preserving distinct concurrent preferences while enforcing shared negative constraints. Experiments on three Amazon domains and evaluations derived from ReDial demonstrate competitive next-item recommendation performance and effective intent refinement. On a challenging target-recovery subset, successive feedback progressively improves target recovery up to 3.22 times, and on ReDial the model achieves strong positive, negative, and multi-intent control. Furthermore, ControlRec supports freely expressed natural-language requests, providing a promising interface for controllable generative recommendation. Code will be released soon, and an interactive online demo is available.