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Development and Evaluation of a Highly Scalable News Recommender System

The development of highly scalable recommender systems, able to deliver recommendations in real time, is a challenging task. In contrast to traditional recommender systems, recommending news entails additional requirements. These requirements include tight response times, heavy load peaks, and continuously changing collections of users and items. In this paper we describe our participation at the CLEFNewsREEL challenge 2015. We present our highly scalable implementation of a news recommendation algorithm. The developed approach alleviates all the specific challenges of news recommender systems. We use the Akka framework to build an asynchronous, distributable system able to run concurrently on multiple machines. Based on the framework a time window-based, most popular algorithm for recommending news articles is implemented. The evaluation shows that our system implemented using the Akka framework scales well with the restrictions and outperforms the recommendation precision of the baseline recommender.