ProtoAdapter: Data-Centric Vertical Adaptation for Pretrained Generative Recommenders

Generative recommenders must adapt pretrained models to new verticals, where item-token alignment alone does not teach how users transition between items and target-domain interactions are often scarce. Existing continual pretraining and supervised fine-tuning primarily learn from individual histories, leaving two complementary signals implicit: recurring behavioral patterns shared across the domain and history-specific evidence that identifies a user's current position within such a pattern. We propose ProtoAdapter, an offline data construction framework for vertical adaptation that makes both signals explicit. ProtoAdapter builds a bank of representative behavioral prototypes from the target-domain corpus, retrieves compatible prototypes for each user history, and selects supporting events from that history. A teacher model then organizes them into a structured trace containing the vertical pattern, observed evidence, and transition bridge, with the target action appended as the supervised answer. Trace-augmented and direct-answer examples are jointly used for continual pretraining and supervised fine-tuning, while deployment retains direct item-token generation without requiring traces. Experiments on several recommendation datasets with Qwen3 and OneRec show consistent improvements across ranking metrics. The gains become more pronounced in low-data settings: on KuaiRand, using only 10% of the training data yields a relative improvement of up to 29.7%. Further analyses verify the effects of trace construction and demonstrate that the proposed two-scale supervision improves data efficiency by connecting reusable domain transitions with sequence-specific evidence. Training code: https://anonymous.4open.science/r/ProtoAdapter/.