Abstract

The amount of data on the web is constantly growing. The separation of relevant from less important information is a challenging task. Due to the huge amount of data available in the World Wide Web, the processing cannot be done manually. Software components are needed that learn the user preferences and support users in finding the relevant information. In this work we present our recommender system tailored for recommending news articles. The developed recommender system continuously analyzes a data-stream using the Apache Flink framework, computes recommender models and provides real-time recommendations. The recommendations are optimized on specific news portals and consider the user session. The recommender system analyzes the user-item interactions in real-time and continuously updates the recommender models ensuring that only fresh articles are recommended. We explain the developed architecture of the system and discuss the specific challenges of processing continuous streams. The scalability and the methods for optimizing the parameter configuration are explained. The evaluation in the NewsREEL Living Lab scenario as well as in the offline evaluation shows that our recommender fulfills the requirements and reaches a good recommendation performance.

@inproceedings{CiobanuLommatzsch:DevelopmentOfANewsRecommenderSystemBasedOnApacheFlink,
author = {Alexandu Ciobanu and Andreas Lommatzsch},
title = {Development of a News Recommender System based on Apache Flink},
booktitle = {{Working Notes of the 7th International Conference of the CLEF Initiative}},
year = {2016},
location = {Evora, Portugal},
pages = {606-617},
issn = {1613-0073},
volume={1609},
publisher = {CEUR Workshop Proceedings},
note = {http://ceur-ws.org/Vol-1609/16090606.pdf}
}