Recommender systems often fall short because they heavily rely on similarities to past behavior, which doesn't account for users' desire for diversity and novelty. Additionally, there can be issues with algorithm biases, such as recency and popularity, and limited utility of machine learning models in practical implementation despite significant research advancements. This leads to less satisfying user experiences.