How do I implement a content-based recommender system?

How do I implement a content-based recommender system?

To implement a content-based recommender system, start by collecting data on the items you want to recommend. This could include features like genre, description, and user ratings.

What kind of data do I need for the items?

You'll need descriptive features for each item, such as keywords, categories, and any other relevant attributes that can help in comparing items.

How do I calculate similarity between items?

You can use techniques like cosine similarity or Euclidean distance to measure how similar two items are based on their features.

Can you give me an example of how to implement this in Python?

Sure! You can use libraries like pandas for data manipulation and scikit-learn for calculating similarities. Would you like a code snippet?