Overview of Building Product Recommendation System
- Production Recommender system is a useful information tool based on algorithms to provide customers with the most suitable products.
- Recommendation lists are based on user preferences, item details, past user interactions.
Challenges in Building Recommendation System
- Collection of data
- Storing the data
- Analyzing the data
- Filtering the data
- Missing Data Values
Solution Offerings for Building Recommendation Systems
- Build Recommendation platform which should not be only Collaborative or Content-Based also including other features linked with Hybrid Recommendation system.
- Link Analysis – Link Analysis collects information about the social circle of customers.
- A Transaction between a customer and Merchant Tracking (graph database)- This tracking will lead to data which not only performs recommendation but others also. Tracking gathers information about customer more likely if customer frequently travels.
- Content-Based Filtering – Content-Based filtering recommends products similar to what customer already owns.
- Collaborative Filtering – Collaborative filtering tracks pattern in customer’s behavior related to other customers so that if products match then, it recommends a new product to the customer with similar liking.