Real-Time IoT Recommendation

Real-Time Recommendation System extracts Live ClickStreams and performs Apache Spark/ Apache Kafka Streaming on it through Revamp, Refine and Supervise. Executes Queries in NoSQL and produces Live Recommendations. Similarly, Batch Processing or Historical Data ClickStreams extraction results in Live Recommendations.

IoT combined with Recommendation gives a better analysis of users engrossment. Role of a Recommendation Engine is to execute injunctions. Recommendations filter the content and display the data which appeals to the particular user. Recommendations fall under two category-

  • Characteristic Based Recommendation include keywords, categories.
  • User Based Recommendation include Ratings, likes, followers.

Product Recommendation System involves analysis and showing items that user would like to purchase.

Recommendation Systems are capturing the markets and flooding every application with suggestions based on Content Based, Knowledge Based, Hybrid, Demographic and Utility-Based Filtering. The recommendation is a form of personalization, but not vice versa.

Real-Time IoT Recommendation Examples Include –

  • Social Media Recommendations covering Facebook, YouTube, Instagram
  • Music and E-Commerce Sites and Applications
  • Google Search and Voice Application
  • IoT Sensor Devices
  • Google Maps and Cab Applications