Abstract

Recommending news articles is a challenging task due to the continuous changes in the set of available news articles and the context-dependent preferences of users. Traditional recommender approaches are optimized for analyzing static data sets. In news recommendation scenarios, characterized by continuous changes, high volume of messages, and tight time constraints, alternative approaches are needed. In this work we present a highly scalable recommender system optimized for the processing of streams. We evaluate the system in the CLEF NewsREEL challenge. Our system is built on Apache Spark enabling the distributed processing of recommendation requests ensuring the scalability of our approach. The evaluation of the implemented system shows that our approach is suitable for the news recommenation scenario and provides high-quality results while satisfying the tight time constraints.

@inproceedings{DomannEtAl:Real-timeNewsRecommendationUsingApacheSpark,
author = {Jaschar Domann and Jens Meiners and Lea Helmers and Andreas Lommatzsch},
title = {Real-time News Recommendation using Apache Spark},
booktitle = {{Working Notes of the 7th International Conference of the CLEF Initiative}},
year = {2016},
location = {Evora, Portugal},
numpages = {14},
issn = {1613-0073},
volume={1609},
pages= {628-641},
publisher = {CEUR Workshop Proceedings},
note = {http://ceur-ws.org/Vol-1609/16090628.pdf}
}