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In this paper, we create an information tree pertaining to the natural user’s communication in the real world to ascertain the user’s interests. This is performed by analysing users’ twitter posts or tweets and comparing them with Wikipedia to generate a graph tree, with nodes pertaining to topics matched. The generated Lifetree is dynamic in nature and is progressed as the continuing users’ communication i.e. is appended to the Lifetree. The various uses of the Lifetree included an overall picture of particular users’ interests and further helps in event allocation, ads customization, etc... Hence, a novel approach for representing users’ data has been proposed, which makes the process of recommendation easier and more accurate. To achieve this, knowledge base and machine learning algorithms have been proposed and utilized.

Lifetree: Building and Comparison based on User’s Tweets