Optimizing and Evaluating Stream-based News Recommendation Algorithms

AutorSebastian Werner, Andreas Lommatzsch
QuelleCLEF 2014 
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Due to the overwhelming amount of items and information users need support in finding the information matching the individual preferences and expectations. Real-time stream-based recommender systems get in the focus of research allowing the adaption of recommendations to the user's context and the current set of relevant items. In this paper we focus on recommending news articles. In contrast to most traditional recommender systems, our system must handle several additional challenges: News articles have a short lifecycle forcing the recommender system to continuously adapt to the set of news articles. In addition, the recommender algorithms should work efficiently: On the one hand, news recommendations must be provided within milliseconds since the recommendations must be embedded in news article pages. On the other hand, the news algorithms must be able to handle a huge amount of recommendation request in order to process load peaks without violating the time constraints. We present algorithms optimized for providing real-time news recommendation given limited hardware resources. We present an offline evaluating framework allowing us the efficient optimizing of recommender algorithms taking into account the available hardware resources. The evaluation shows that our approach allows us to find optimal recommender algorithms for a given hardware setting.