Final answer:
The statement is false; recommendation engines can be susceptible to feedback bias due to the quality and type of data inputs.
Step-by-step explanation:
The statement that recommendation engines are not susceptible to feedback bias due to constraints on data inputs is False.
Recommendation engines use algorithms that rely on user data to make predictions and suggestions. The quality and type of data inputs can significantly affect the performance of these systems. If the data inputs are biased, the recommendations will likely reflect that bias. This is because algorithms can inadvertently learn and amplify biases present in their training data, which is known as feedback bias. Essentially, biased user interaction leads to biased data, which in turn leads to more biased recommendations, creating a feedback loop. Therefore, recommendation engines can indeed suffer from feedback bias if the data they are fed is not carefully curated and monitored.