Personalized Image Generation for Recommendations Beyond Catalogs
Abstract
Retrieval-based recommender systems are constrained by fixed catalogs, limiting their ability to serve diverse and evolving user preferences. We propose REBECA (REcommendations BEyond CAtalogs), a new class of preference-aware generative models for recommendation that synthesizes images tailored to individual tastes rather than retrieving items. REBECA conditions a diffusion model on users’ feedback (e.g., ratings) to generate personalized image embeddings in CLIP space, which are decoded into images via an adapter-on-adapter architecture that bypasses the need for image captions during training. By leveraging an expressive pre-trained image decoder and a lightweight probabilistic adapter, REBECA enables general-purpose image generation aligned with users’ visual preferences across diverse domains without expensive fine-tuning. We also introduce a new benchmark for personalized generation based on a curated version of the FLICKR-AES dataset, along with two novel personalization metrics tailored to the generative setting. Empirical results show that REBECA produces high-quality, diverse, and preference-aligned outputs, outperforming prompt-based personalization baselines on key personalization and quality metrics. By augmenting traditional retrieval with generative modeling, REBECA opens new opportunities for applications such as content design, personalization-first creative platforms, and preference-aware synthetic media.
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