Final answer:
User-based recommender systems face several disadvantages such as difficulty in analyzing large data sets, parameterization challenges, issues with user communication, and limitations imposed by software and hardware requirements.
Step-by-step explanation:
Disadvantages of User-Based Recommender Systems
User-based recommender systems, while useful, also have several disadvantages. One of the limitations is the difficulty to analyze the large amounts of data they handle, making it challenging to determine the best approach for recommendations. Furthermore, these systems are often difficult to parameterize; finding the right settings for personalized recommendations requires significant tuning and experimentation.
Another drawback is the difficulty to communicate complex, multi-dimensional data to end-users in a comprehensible way. The recommendations need to not only be accurate but also understandable by the users to ensure trust in the system. Lastly, user-based recommender systems are often limited by software and hardware requirements. As they typically process vast quantities of data, the computational resources needed to run these systems efficiently can be extensive, resulting in additional costs and complexities.