Semantic User Interaction Profiles for Better People Recommendation
In this paper, the authors present a methodology for learning user profiles from content shared by people on Social Platforms. Such profiles are specifically tailored to reflect the user's degree of interactivity related to the topics they are writing about. The main novelty in the work is the introduction of Linked Data in the content extraction process and the definition of specific scores to measure expertise and interactivity. The analysis of shared content on social platforms may provide a new input for advanced recommendation strategies, as it offers valuable insights into people's interests, plans, findings and information needs.