A Framework for Analyzing News Images and Building Multimedia-based Recommenders

AutorAndreas Lommatzsch, Benjamin Kille, Kevin Styp-Rekowski, Max Karl, Jan Pommering
Quelle19th International Conference on Innovations for Community Services (I4CS), Wolfsburg, Germany 
LinksBibTeX   |   Uni-Bibliothek 

The number and accessibility of published news items have grown recently. Publishers have developed recommender systems supporting users in finding relevant news. Traditional news recommender systems focus on collaborative filtering and content-based strategies. Unlike texts, multimedia content has received little attention. However, images and other multimedia elements affect how users perceive the news. In this work, we present a system that aggregates text-based, image-based, and user interests-based features to foster recommender systems for news. The system monitors a live stream of news and interactions with them. It applies text analysis and automatic image labeling methods for enriching the news stream. A web application visualizes the collected data and statistics. We show that image features are valuable for developing news recommender systems. The created feature-rich dataset constitutes the basis for developing innovative news recommendation approaches.