"Cold-start" problem in Azure Recommender

Question

Hi, I've trained and deployed an Azure Wide & Deep Recommender in the Designer tab of the Machine learning workspace.
I have 3 datasets:

• Ratings
• User features
• Item features

The recommendation system works fine for existing user IDs available in User Features and Rating datasets.
However, when a want to make a prediction for a new user to the system, which IDs were not used during training, I get an error.

Is that expected that the recommendations are made only for users that the model learned during training? If not, please help me resolve this issue, so that even "cold" users can receive recommendations.

The model training and real-time inference pipelines are attached. Also, included the deployment logs with errors when sending a request to the model to make recommendations for new users.

103326-ci-error-logs-recommender-system.txt

Thanks!