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The traditional offline approaches are no longer sufficient for buildingmodern recommender systems in domains such as online news services, mainly dueto the high dynamics of environment changes and necessity to operate on a largescale with high data sparsity. The ability to balance exploration withexploitation makes the multi-armed bandits an efficient alternative to theconventional methods, and a robust user segmentation plays a crucial role inproviding the context for such online recommendation algorithms. In this work,we present an unsupervised and trend-responsive method for segmenting usersaccording to their semantic interests, which has been integrated with areal-world system for large-scale news recommendations. The results of anonline A/B test show significant improvements compared to a global-optimizationalgorithm on several services with different characteristics. Based on theexperimental results as well as the exploration of segments descriptions andtrend dynamics, we propose extensions to this approach that address particularreal-world challenges for different use-cases. Moreover, we describe a methodof generating traceable publishing insights facilitating the creation ofcontent that serves the diversity of all users needs.

Trend-responsive User Segmentation Enabling Traceable Publishing Insights. A Case Study of a Real-world Large-scale News Recommendation System