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Enterprise AI

Graph Database for Recommendation Systems

Dr. Jagreet Kaur Gill | 01 March 2017

What is a recommendation system?

A recommendation system is any rating system that anticipates a person's preferred options based on the information at hand. Recommendation systems are used in many different services, including social media, online commerce, and video streaming. In most cases, the system gives users recommendations based on its projection of the rating a user will give a product. Two characteristics of recommendation systems can be grouped together: the information used and the prediction models.

Overview of Building Product Recommendation System

  • Graph Database for Recommendation provides customers with the most suitable products.
  • Recommendation lists are based on user preferences, item details, and 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 a Recommendation platform that should not be only Collaborative or Content-Based but also include other features linked with a 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 that 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 customers’ behavior related to other customers so that if products match, then it recommends a new product to the customer with similar liking.

Complete Guide to Graph Database

Graph Database understands, predicts, and model exploring highly connected data leveraging organization. It involves quicker access to data, data import, Data Integration, graph algorithms for pathfinding, and Data Ingestion.

Graph Database Services and Solutions involve -

  • Artificial Intelligence Machine Learning
  • PageRank Graph Algorithm
  • Graph Processing Engine
  • Fraud Detection
  • Knowledge Graph
  • Network and Infrastructure Monitoring
  • Real-Time Recommendation and Product Recommendation Engine
  • Master Data Management
  • Social Media and Social Network Graphs
  • Regulatory Compliance
  • Identity and Access Management
  • Supply Chain Transparency
  • Connected Data Platform
  • Native Graph Cloud Compute
  • Graph Block Storage
  • Connected Data Architecture
  • Analytics and Transactions
  • Enrichment Discovery Design and Operational Activities
  • Hybrid Frameworks

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