Click4Ai

529.

Hard

Click-through rate (CTR) prediction is a crucial task in recommender systems. We have a dataset of user-item interactions, where each interaction is represented as a tuple of user ID, item ID, and click status (1 for clicked, 0 for not clicked). We want to predict the CTR for each interaction.

Example:

We have the following dataset:

user_item_interactions = [(1, 1, 1), (1, 2, 0), (2, 1, 1), (2, 3, 0), (3, 1, 1), (3, 2, 0), (3, 4, 1), (4, 1, 0), (4, 2, 1), (4, 3, 0)]

Constraints:

  • The dataset is represented as a list of tuples, where each tuple contains user ID, item ID, and click status.
  • The click status is an integer between 0 and 1.
  • Write a function to predict the CTR for each interaction in the dataset.

    Test Cases

    Test Case 1
    Input: [[1, 1, 1], [1, 2, 0], [2, 1, 1], [2, 3, 0], [3, 1, 1], [3, 2, 0], [3, 4, 1], [4, 1, 0], [4, 2, 1], [4, 3, 0]]
    Expected: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5]
    Test Case 2
    Input: [[1, 1, 1], [1, 2, 0], [2, 1, 1], [2, 3, 0], [3, 1, 1], [3, 2, 0], [3, 4, 1], [4, 1, 0], [4, 2, 1], [4, 3, 0]]
    Expected: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5]
    + 3 hidden test cases