Abstract

The analysis of images in the context of recommender systems is a challenging research topic. NewsREEL Multimedia enables researchers to study new algorithms with a large dataset. The dataset comprises news items and the number of impressions as a proxy for interestingness. Each news article comes with textual and image features. This paper presents data characteristics and baseline prediction models. We discuss the performance of these predictors.

@inproceedings{LommatzschKille:AlgorithmsForPredictingThe InterestInNewsBasedOnMultimediaData,
author = {Andreas Lommatzsch and Benjamin Kille},
title = {Baseline Algorithms for Predicting the Interest in News based on Multimedia-Data},
booktitle = {Proceedings of MediaEval 2018},
year = {2018},
issn = {1613-0073},
url = {http://ceur-ws.org/Vol-2283/},
location = {Sophia Antipolis, France},
publisher = {CEUR Workshop Proceedings},
}