Abstract

The huge amount of news articles published every hour makes it hard for users to find the relevant news matching the user's expectations. The main challenges when developing a recommender for the news domain are the continuous changes in the set of items, the context-dependent relevance of items, as well as the requirements with respect to scalability and response time. In this work, we present a scalable and distributable implementation of a real-time news recommender system based on the Akka framework. Our approach focuses on optimizing the recommendation precision. It is able to adapt to the continuous changes of the set of relevant news articles as well as it considers the different user preferences dependent from the hour the day. Our implementation ensures that tight response time constraints are fulfilled and the system can be easily extended to streams of much larger volume. We implement three different recommendation algorithms namely, Most Popular Items, Most Recent Items, and Most Recent Items of the Most Popular Categories. A time-dependent delegation strategy is used for assigning requests to a recommender algorithm. We evaluate the developed recommender system in the CLEF-NewsREEL challenge 2016. The evaluation shows that the recommender performs very successfully; the developed recommender has won the online evaluation in several timeframes.

@inproceedings{ProbstLommatzsch:OptimizingAScalableNewsRecommenderSystem,
author = {Patrick Probst and Andreas Lommatzsch},
title = {Optimizing a Scalable News Recommender System},
booktitle = {{Working Notes of CLEF 2016 - Conference and Labs of the Evaluation forum}},
year = {2016},
location = {Evora, Portugal},
pages = {669-678},
issn = {1613-0073},
volume={1609},
publisher = {CEUR Workshop Proceedings},
note = {http://ceur-ws.org/Vol-1609/16090669.pdf}
}