E-commerce businesses are always striving to provide personalized experiences to their customers to increase engagement and loyalty. One way to achieve this is through product recommendation systems. In this project, we will build a recommendation system for an e-commerce website using batch processing and stream processing techniques.
To start, we will use batch processing to preprocess the data. We will collect data on user behaviour, such as clicks, purchases, and page views, and use this data to build user profiles. These user profiles will be used to generate recommendations based on user preferences and behavior.
Once the initial batch processing is complete, we will move on to stream processing. We will use real-time data, such as user searches and clicks, to update the recommendations in real time. This means that as users browse the site, the recommendations they see will change based on their current activity.
To build the recommendation system, we will use machine learning algorithms such as collaborative filtering, content-based filtering, and hybrid filtering. Collaborative filtering analyzes user behavior to find patterns and make recommendations based on similar users' behavior. Content-based filtering analyzes the characteristics of the products to recommend products that are similar to those that the user has already purchased or viewed. Hybrid filtering combines both collaborative and content-based filtering to provide more accurate recommendations.
In conclusion, by combining batch processing and stream processing techniques and using machine learning algorithms, we can build a product recommendation system that provides personalized recommendations to users based on their behaviour and preferences. This can enhance the user experience, increase engagement, and ultimately lead to higher sales.