A workflow for cross media recommendations based on linked data analysis

The quality of content-based recommendation depends to a very high degree on the quality of the metadata available. We propose a workflow that combines novel cross media analysis platforms with linked data analysis to generate recommendations. The focus is set on an "editor user story" that combines live analysis of currently created content with a stored data backlog to select suitable content for article enrichment.