Real-world recommender systems often suffer from extreme imbalance in user feedback, as most users provide only positive opinions. This paper demonstrates that such imbalance has substantial negative effects on the system’s ability to generalize, particularly in predicting negative preferences. To address this issue, the study investigates whether intra-domain transfer learning, leveraging users with more complete profiles (those who provide both positive and negative feedback) can help mitigate these limitations. A synthetic evaluation framework is introduced based on false-positive and false-negative user profiles, allowing for a detailed assessment of model behavior under incomplete user data. Experiments on image-based datasets reveal consistent improvements in true negative rates for underrepresented user groups. Although the mitigation is partial, the results suggest that selecting appropriate user subsets as a source domain may offer a promising direction, and that identifying which users generalize best could be key to future progress.