Training vs test data set for supervised learning in real life scenario
One important assumption of data for a lot of Machine Learning algorithms is that the data from the training set and test set are i.i.d (independent and identically distributed) and come from the same probability distribution. More explanation here. That said, you cannot train a model for movies recommendation and use for house rentals. Or not that I'm aware of.

However, it's possible to train a model for house rentals recommendation if you have a lot of data for movies recommendation and a small amount of data for house rentals recommendation, using a technique called Transfer Learning. The idea is simple: If two tasks are similar, we should be able to use what we learn from one task to learn the other task faster and better.

Following is the general process (for neural networks):


Using movies data, train a model for movies recommendation
Fine-tune this recommendation system using house rentals data: Freeze the most part of the movies recommender, retrain a few last layers with house rentals data.


This is just a direction about how it can be done, there's quite a lot of related literature for you to learn more. I would say this is a not very uncommon scenario.