Recurrent Neural Networks (RNNs) are crucial for processing sequential data, especially in recommendation systems. They help define temporal dynamics and user behavior patterns, making them ideal for platforms like YouTube to recommend content based on viewing history. RNNs can also build session-based recommendations without needing user identification, predicting future actions from click history. Their importance lies in capturing the sequential nature of user behavior, which is essential for effective recommendation systems.

What challenges do websites face regarding user identification for recommendations?

How can RNNs help in predicting user actions based on their click history?