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 recommendations but others also. Tracking gathers information about the customer more likely if a customer frequently travels.
Content-Based Filtering – Content-Based filtering recommends products similar to what the customer already owns.
Collaborative Filtering – Collaborative filtering tracks patterns 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.