Recommendation System for products & merchandising
Based on my research during my thesis, the project aimed at building a product recommendation system using three distinct models: Collaborative Filtering, Content-Based, and Session-Based Recommendations.
This project involved the complete analysis, design, and development of a recommendation system, executed in collaboration with a Data Scientist. Drawing heavily on research conducted during my thesis, I worked on all aspects of the system, including R&D, the structure and training of Machine Learning (ML) models, and the development of the platform. The system was designed to recommend products based on user preferences, behavior, and interaction with the platform.
Key Project Objectives:
- Research & Development (R&D): A significant portion of the project focused on R&D to explore the best algorithms for product recommendation. We relied on insights from the research conducted during my thesis, which provided a foundation for designing the system.
- Model Development: The recommendation system was based on three key models:
- Collaborative Filtering: Using historical user interaction data to recommend products based on similarities between users.
- Content-Based: Leveraging product attributes to recommend similar products based on what the user has interacted with or purchased.
- Session-Based: Recommending products based on the current session activity, allowing for real-time suggestions that adapt to the user's immediate interests.
- System Architecture: The architecture was designed for scalability and efficiency, ensuring that the recommendation engine could handle high volumes of traffic and provide real-time suggestions. We integrated the different models into a unified recommendation pipeline that efficiently processed and returned personalized suggestions.
- Training of Machine Learning Models: The machine learning models used in the system were carefully trained using historical data, ensuring that the recommendations were both relevant and accurate. We optimized these models for performance, ensuring minimal latency in real-time use.
- System Development: In addition to the research and machine learning work, I handled the development of the entire system, integrating the recommendation engine with the backend and ensuring its smooth operation in a production environment. This included optimizing APIs, implementing user-facing features, and ensuring high availability and low latency.
Project Approach:
- Needs Analysis and Requirements Gathering: We began by understanding the needs of the platform and defining the key requirements for the recommendation system. This involved discussions with stakeholders to ensure we were solving the right problem and providing a user-centric experience.
- Model Selection and Evaluation: Based on research and initial prototypes, we selected the three recommendation models: Collaborative Filtering, Product Content, and Session-Based models. Each was tested and evaluated against a set of metrics to determine its effectiveness in delivering high-quality recommendations.
- Model Training and Tuning: The next phase focused on training the models with real data. We iterated through different algorithms and fine-tuned parameters to improve the accuracy and relevance of recommendations. The models were evaluated and adjusted based on continuous feedback from testing and real-world usage.
- System Integration and Development: The recommendation models were integrated into a seamless architecture that allowed the backend to efficiently deliver personalized product recommendations. The system was built with scalability in mind to support future growth and increasing data complexity.
- Testing and Deployment: Extensive testing was conducted to validate the system's performance under real-world conditions. The system was deployed and monitored for optimization and continued refinement based on user feedback and model performance.
Results and Benefits:
- Highly Accurate Recommendations: The combination of Collaborative Filtering, Product Content, and Session-Based models delivered accurate and personalized product recommendations tailored to individual users.
- Real-Time Processing: The system was designed to process user data in real-time, providing dynamic product suggestions based on ongoing sessions and interactions.
- Scalable Solution: The architecture was built to handle growing amounts of data, ensuring the system can scale alongside an expanding user base without sacrificing performance.
- Optimized User Experience: By integrating multiple recommendation techniques, the system was able to create a more engaging and relevant user experience, ultimately improving user retention and sales.
Conclusion:
This project resulted in the successful development of a robust and efficient product recommendation system, capable of providing personalized recommendations based on user behavior, preferences, and session activity. The collaboration with the Data Scientist ensured a data-driven approach while leveraging my research and technical expertise. The system is built to scale, ensuring that it will continue to meet the needs of the platform as it grows and evolves over time.
