Product Recommendation with Graph Database

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.
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