Memoire: Learning User Personas from Gallery Tags for Personalized Photo Curation

We introduce Memoire, a fully automatic, on-device system for personalized photo selection that learns a user’s persona directly from gallery tags—people & relations, locations, and events—and ranks images by personal impact rather than generic aesthetics. Memoire constructs a per user tag graph and trains PERSONA-GAT to produce tag importance scores summarizing user preferences across the gallery images. These scores are projected to pixels via PAT, a deterministic grounding module that fuses tags and their importance into personal attention maps. To obtain scalable supervision without collecting user labels, we synthesize virtual-user galleries (diverse identities, events, and locations) and use a vision–language model to annotate image pairs as High vs. Low personal impact conditioned on their personal attention maps. An impact predictor is then trained with a pairwise ranking loss and coupled with a diversity-aware selector to deliver non-redundant top-k image selection. To maintain user privacy, persona learning and inference run entirely on device. Synthetic data and VLM supervision are used only for training of impact predictor. On a real 100-user gallery study, Memoire outperforms strong aesthetics, memorability and multimodal baselines.