518.518. Content-Based Filtering

Implement a content-based filtering algorithm to recommend items to users. Content-based filtering is a type of recommendation algorithm that uses the attributes of an item to make recommendations.

Example:

Suppose we have a dataset of item attributes, where each row represents an item and each column represents an attribute. The value in each cell represents the value of the attribute for the item.

Constraints:

The item attribute data is provided in a numpy array of shape (n_items, n_attributes), where n_items is the number of items and n_attributes is the number of attributes.

Your task is to implement a content-based filtering algorithm that takes the item attribute data as input and outputs a matrix of recommended items for each user.

You can use the sklearn library to implement the content-based filtering algorithm. You will need to define the algorithm and the parameters used to train it.