Semantic user profile enrichment in collective intelligence context: A Healthcare case study

Abstract

The Personalized systems are generally based on collecting and exploiting users' preferences by exploring their traces' data. Actually, they find users who have similar attributes, cluster them, and then applying algorithms using the subnets. The similarity between users compares their profiles including their attributes. The adding of tags to enrich the user profile must take into consideration the long and short term criteria of the user's attributes that change over time. In this paper, we present a tag-based profile enrichment approach by adding a time score describing the short and long term criteria of the attribute. Then we use graph analytics to draw clusters of users by inspecting similar tags. Our approach helps companies to make their predictions and conclusions. The datasets of patients' images ChestX-Ray14 have been conducted to evaluate the effectiveness of our approach.