Abstract

Recommender systems typically analyze the user behavior to predict the items most likely matching the user preferences. While analyzing the user preferences the observed data must be normalized considering characteristic usage pattern as well as the lifecycles of the items. The normalization is especially relevant in domains characterized by frequently updated item sets and fast changes in the set of users, typical in news recommender scenarios. In this paper, we study approaches for normalizing the collected data in a news recommender system. We analyze live usage data from several German news portals available in the NewsREEL challenge. We discuss the influence of seasonality, day of week and hour of day patterns. We show that applying time series decomposition and normalization methods enable us to model the relevant aspects resulting in the observed item popularity. The derived models are the basis for precisely predicting the interest in concrete items in the future.

@inproceedings{CiobanuEtAl-NewsREEL,
author = {Alexandru Ciobanu and Asmaa Haja and Andreas Lommatzsch},
title = {Normalization of Timeseries for Improving Recommendations},
booktitle = {Proc. of the LWDA conference},
year = {2018},
isbn = {ISSN 1613-0073},
pages = {84--92},
location = {Mannheim, Germany},
publisher = {CEUR Workshop Proceedings},
address = {http://ceur-ws.org/},
volume = {2191},
note = {urn:nbn:de:0074-2191-5}
}