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.

Looking For More Details

Download Now

Data Driven Enterprises with DataOps

Talk to Experts for Continuous Delivery to Analytics, Machine Learning and Data Management Practices

Reach Us

Disrupting Industries with Enterprise AI

Accelerate AI Adoption by Harnessing AI Power, Implementing AI Solutions and Leveraging AI Marketplace

Contact Us

NexaOps - Managed Services

  • DevOps Services
  • DataOps Services
  • SecOps Services
  • CloudOps Services
Learn More