Abstract

In recent years, the World Wide Web has changed from text-focused web pages to multi-media sources featuring photos, videos, and audio. The worldwide growth of broadband connections has facilitated this trend and supports the spread of user-generated content. Navigating and finding interesting content has become a difficult challenge. In this paper, we present approaches which use visual features to predict how interesting a news article will be. This task is part of the NewsREEL Multimedia challenge. The challenge provides a large-scale data set of news items, images, and interactions. We implement a recommender system which can distinguish interesting articles from irrelevant ones based on image features. We evaluate the system's throughput and predictions. We explain our insights and outline ideas to apply the gained knowledge in additional domains.

@inproceedings{CiobanuEtAl:InterestPredictionBasedOnImageLabels,
author = {Alexandru Ciobanu and Andreas Lommatzsch and Benjamin Kille},
title = {Predicting the Interest in News based On Image Annotations},
booktitle = {Proc. of the Multimedia Benchmark Workshop 2017},
year = {2018},
isbn = {ISSN 1613-0073, Vol. 22xx},
numpages = {3},
location = {Sophia Antipolis, France},
publisher = {CEUR WS Workshop Proceedings}
}