Real-Time Recommendation Engine for Readers
In today's world, every reader or social media user has different choices/hobbies in terms of reading. For example, if any social media user is searching for a book to read without any specific idea of what s/he wants, s/he wastes a lot of time browsing around on the internet and crawling/trawling through various sites hoping that s/he might get good book. To avoid confusion, the authors are building a recommendation system for every reader/user that helps to recommend a book based on his choices, hobbies, or what s/he had read previously that will be massive help for users instead wasting time on various sites. Data from social media is the powerful fuel that can be used to helps in decision making and building a recommendation engine. Social media data in the different format is biggest challenge for the business to ingest data at the reasonable speed and further process. In social media data, it is difficult to detect and capture data. Real-time recommendation engine for users, which includes data ingestion methods, challenges, metadata problem, analysis, and consumption, is discussed here.