Optimizing and Evaluating Stream-Based News Recommendation Algorithms

Recommender algorithms are powerful tools helping users to find interesting items in the overwhelming amount available data. Classic recommender algorithms are trained based on a huge set of user-item interactions collected in the past. Since the learning of models is computationally expensive, it is difficult to integrate new knowledge into the recommender models. With the growing importance of social networks, the huge amount of data generated by the real-time web (e.g. news portals, micro-blogging services), and the ubiquity of personalized web portals stream-based recommender systems get in the focus of research.

In this paper we develop algorithms tailored to the requirements of a web-based news recommendation scenario. The algorithms address the specific challenges of news recommendations, such as a contextdependent relevance of news items and the short item lifecycle forcing the recommender algorithms to continuously adapt to the set of news articles. In addition, the scenario is characterized by a huge amount of messages (that must be processed per second) and by tight time constraints resulting from the fact that news recommendations should be embedded into webpages without a delay. For evaluating and optimizing the recommender algorithms we implement an evaluation framework, allowing us analyzing and comparing different recommender algorithms in different contexts. We discuss the strength and weaknesses both according to recommendation precision and technical complexity. We show how the evaluation framework enables us finding the optimal recommender algorithm for a specific scenarios and contexts.