### Problem: Session-Based Recommendations
In this problem, we will implement a session-based recommendation system using deep learning. The goal is to predict the next item that a user will interact with based on their past interactions.
**Example:** Suppose we have a user who has interacted with items 1, 2, and 3 in the first session, and items 4 and 5 in the second session. Our model should predict that the user will interact with item 6 next.
**Constraints:** The input will be a list of user interactions, where each interaction is a list of item IDs. The output will be a list of item IDs that the user is likely to interact with next. The model should be able to handle sessions of varying lengths and should be able to learn from the sequence of interactions.
### Solution: Implement a session-based recommendation system using a recurrent neural network (RNN) and a softmax output layer. Use the Keras API to build and train the model.
Test Cases
[[[1, 2, 3], [4, 5, 6]]][[7, 8, 9], [10, 11, 12]][[[1, 2, 3], [4, 5, 6], [7, 8, 9]]][[10, 11, 12], [13, 14, 15], [16, 17, 18]]