@inproceedings{57bdbc2b12f14415a8fe40d7f4b1eecb,
title = "Exploring methods of assessing influence relevance of news articles",
abstract = "Assessing the influence relevance of a news article is a very important and novel task for news personalized recommendation services. It provides a novel functionality by additionally recommending users news articles that may not match users{\textquoteright} interest points but can help users make good decisions in their daily lives. Since the influence of implicit information delivered by news articles cannot be obtained literally, and meanwhile regions and industries affected by the influence of implicit information are usually not explicitly mentioned in news articles, machine-based methods lost their ability. In this paper we explore methods of assessing influence relevance of news articles by employing crowdsourcing, and the experimental results show that crowdsourcing can assess the influence relevance of news articles very well.",
keywords = "Crowdsourcing, Ground truth inference, Influence relevance",
author = "Qingren Wang and Sheng, {Victor S.} and Zhaobin Liu",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 4th International Conference on Cloud Computing and Security, ICCCS 2018 ; Conference date: 08-06-2018 Through 10-06-2018",
year = "2018",
doi = "10.1007/978-3-030-00021-9_47",
language = "English",
isbn = "9783030000202",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "525--536",
editor = "Elisa Bertino and Xingming Sun and Zhaoqing Pan",
booktitle = "Cloud Computing and Security - 4th International Conference, ICCCS 2018, Revised Selected Papers",
}