To implement a recommendation system using collaborative filtering in Java, I would start by collecting user-item interaction data to create a user-item matrix. Then, I'd apply techniques like user-based or item-based collaborative filtering using libraries such as Apache Commons Math or implementing custom algorithms to calculate similarity metrics and generate recommendations based on similar users or items.

Collaborative filtering relies on user behavior and preferences to predict future interests for users. In Java, the implementation typically starts with gathering extensive user-item interaction data, which could include ratings, purchases, or viewing history. The challenge is to efficiently handle sparse data, as many users might not have interacted with all items. Techniques like cosine similarity or Pearson correlation can be applied to find relationships between users or items within this matrix. Moreover, it’s essential to implement strategies to handle cold starts for new users or items that lack sufficient interaction data, which can include hybrid approaches that incorporate content-based filtering as well.