PolyRecs: Improving Page–View Rates Using Real-Time Data Analysis

  • Mihalis PapakonstantinouEmail author
  • Alex DelisEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 337)


In this paper, we outline our effort to enhance the page-view rates of e-content that online customers read on a popular portal in Greece. The portal,, provides continuous coverage on news, politics, science, the arts, and opinion columns and its customers generate approximately 6 million unique visits per month. Gains both in terms of advertisement and further e-content market penetration were the objectives of our effort which yielded the PolyRecs system, in production for more than a year now. In designing PolyRecs, we were primarily concerned with the use of pages in real-time and to this end, we elected to utilize five key criteria to achieve the aforementioned goals. We selected criteria for which we were able to obtain pertinent statistics without compromising performance and offered a real-time exploitation of the user page-views on the go. In addition, we were keen in realizing not only effective on-the-fly calculations of what might be interesting to the browsing individuals at specific points in time but also produce accurate results capable of improving the user-experience. The key factors exploited by PolyRecs entail features from both collaboration and content-based systems. Once operational, PolyRecs helped the news portal attain an average increase of 6.3% of the overall page-views in its traffic. To ascertain the PolyRecs utility, we provide a brief economic analysis in terms of measured performance indicators and identify the degree of contribution each of the key factors offers. Last but not least, we have developed PolyRecs as a domain-agnostic hybrid-recommendation system for we wanted it to successfully function regardless of the underlying data and/or content infrastructure.


Timely delivery of news articles Content-based furnishing of news Hybrid recommendation systems Real-time content analysis 



we are grateful for the reviewer comments received; partial support for this work has been provided by the GALENA EU Project and Google.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of AthensAthensGreece

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