Recommendation engines use predictive algorithms to suggest recommendations to a bunch of users. It is a powerful technology, but we should be aware of its limitations. Let's look into the various limitations of recommendation systems.
Understanding the limitations of recommender systems
The cold start problem
It is obvious that, for collaborative filtering to work, we need to have historical data about user preferences. For a new user, we may not have any data, so our user similarity algorithm will be based on assumptions that may not be accurate. For content-based recommendations, we may not have the details about the new items right away. This requirement of having data about items and users to generate high-quality...
