Implementing a recommendation system with Java and Apache Mahout involves using Mahout’s machine learning algorithms and collaborative filtering techniques to build personalized recommendation systems. Here’s an overview of the steps involved:

1. Set up the Development Environment:
   Install Java Development Kit (JDK) and an Integrated Development Environment (IDE) like IntelliJ IDEA or Eclipse.
2. Include Apache Mahout Dependencies:
   Add the necessary dependencies for Apache Mahout in your Java project. You can include Mahout as a Maven or Gradle dependency, or manually add the Mahout library to your project.
3. Data Collection and Preprocessing:
   Gather the relevant data for your recommendation system, such as user preferences, item features, or historical data. Preprocess the data by cleaning, transforming, and preparing it for model training.
4. Choose a Recommendation Algorithm:
   Apache Mahout provides various recommendation algorithms, including collaborative filtering, content-based filtering, and matrix factorization. Choose the algorithm that suits your requirements and data characteristics.
5. Create a Data Model:
   Use Mahout’s data model classes to represent your data, such as DataModel, Preference, and Item.
6. Train the Recommendation Model:
   Use Mahout’s recommendation algorithm classes to train the recommendation model using your prepared data. Configure the algorithm parameters and feed the data into the algorithm to generate the model.
7. Evaluate the Model:
   Evaluate the performance of your recommendation model using evaluation metrics like precision, recall, or mean average precision. This step helps you measure the accuracy and effectiveness of your model.
8. Generate Recommendations:
   Once the model is trained and evaluated, use the recommendation algorithm to generate personalized recommendations for users. Provide user IDs or item IDs as input to the algorithm and obtain the recommended items or user-item predictions.
9. Implement Recommendation Engine in your Application:
   Integrate the recommendation engine into your Java application. This can involve exposing recommendation endpoints or methods for users to request recommendations, and displaying the recommendations in your application’s user interface.
10. Monitor and Refine the System:
    Continuously monitor the performance of your recommendation system and collect user feedback. Refine the model periodically by retraining it with new data or using techniques like online learning to adapt to evolving user preferences.

Deployment:
Deploy your Java application with the implemented recommendation system to a server or cloud platform of your choice. Ensure that the necessary resources and dependencies, including the Mahout library, are properly configured and accessible.

Implementing a recommendation system with Java and Apache Mahout enables you to provide personalized recommendations to users, improving user engagement and satisfaction. Remember to consider scalability, performance, and data privacy while building and deploying your recommendation system.