Setting up a recommendation system using Hadoop can be a powerful way to process large amounts of data and generate personalized recommendations efficiently. Hadoop is a framework that enables the distributed processing of massive datasets across clusters of computers. Here’s a high-level overview of how you could approach building a recommendation system using Hadoop:

1. Data Collection and Preparation: Gather the relevant data for your recommendation system. This could include user interactions, item data, and other relevant information. Prepare and preprocess the data to make it suitable for analysis.
2. Data Storage: Hadoop’s HDFS (Hadoop Distributed File System) can store the large datasets required for recommendation systems. The data is stored in a distributed manner across the Hadoop cluster.
3. Data Processing: Utilize Hadoop’s MapReduce programming model to process the data. This involves writing Map and Reduce functions that process and aggregate the data to generate user-item interaction matrices, similarity matrices, or any other relevant data structures.
4. Collaborative Filtering: Collaborative filtering is a common approach in recommendation systems. It involves using user behavior (such as ratings or purchase history) to find similarities between users or items. Implement collaborative filtering algorithms using Hadoop’s MapReduce, such as User-Based or Item-Based Collaborative Filtering.
5. Content-Based Filtering (Optional): Content-based filtering recommends items to users based on the attributes of the items and the user’s preferences. This can be implemented alongside collaborative filtering. Use Hadoop to process item attributes and generate recommendations.
6. Model Training (Optional): More advanced techniques like matrix factorization or deep learning models can also be used for recommendation systems. If you use machine learning models, Hadoop can distribute training tasks across the cluster.
7. Generating Recommendations: Once the collaborative and content-based filtering models are trained, apply them to the user-item interaction data to create personalized user recommendations.
8. Deployment and Scalability: Hadoop allows you to scale your recommendation system as your dataset grows. You can add more nodes to your cluster to handle larger datasets and higher user activity.

To ensure that your email notifications related to this system don’t end up in spam, consider the following tips:

• Use a reliable email service provider.
• Set up proper DKIM and SPF records for your domain to improve email deliverability.
• Craft relevant and non-spammy subject lines and email content.
• Avoid excessive use of images or links.
• Provide an option for recipients to unsubscribe.
• Regularly monitor your email campaign’s performance and adjust as needed.