Recommender Ensembles for News Articles based on Most-Popular Strategies

With the change from classical paper-based to online representations of newspapers publishers are able to provide adaptive recommender services supporting users in finding the relevant articles in the huge amount of published news. Since most traditional recommender approaches are tailored to scenarios characterized by static sets of items and exactly identifiable users, these approaches cannot be utilized for anonymously consumed news portals offering a continuously changing set of items. In order to develop an efficient recommender system optimized to the specific requirements of news portals, we analyze the user behavior and define a model for computing recommendations. We have developed tools for continuously monitoring the user preferences and analyzing the features of the most popular news articles. The derived recommender models are implemented in Java using a recommender ensemble architecture that is able to adapt to the specific characteristics of different news portals. The evaluation of the implemented recommender in the CLEF NewsREEL challenge shows that our system provides reliable results and reaches a high Click-Through-Rate. The implemented architecture is open to further optimization with respect to different load levels and context parameters.