Where Are the Values? A Systematic Literature Review on News Recommender Systems

Author:

Bauer Christine1ORCID,Bagchi Chandni2ORCID,Hundogan Olusanmi A.2ORCID,van Es Karin2ORCID

Affiliation:

1. Paris Lodron University Salzburg, Salzburg, Austria

2. Utrecht University, Utrecht, Netherlands

Abstract

In the recommender systems field, it is increasingly recognized that focusing on accuracy measures is limiting and misguided. Unsurprisingly, in recent years, the field has witnessed more interest in the research of values “beyond accuracy.” This trend is particularly pronounced in the news domain where recommender systems perform parts of the editorial function, required to uphold journalistic values of news organizations. In the literature, various values and approaches have been proposed and evaluated. This article reviews the current state of the proposed news recommender systems (NRS). We perform a systematic literature review, analyzing 183 papers. The primary aim is to study the development, scope, and focus of value-aware NRS over time. In contrast to previous surveys, we are particularly interested in identifying the range of values discussed and evaluated in the context of NRS and embrace an interdisciplinary view. We identified a total of 40 values, categorized into five value groups. Most research on value-aware NRS has taken an algorithmic approach, whereas conceptual discussions are comparably scarce. Often, algorithms are evaluated by accuracy-based metrics, but the values are not evaluated with respective measures. Overall, our work identifies research gaps concerning values that have not received much attention. Values need to be targeted on a more fine-grained and specific level.

Funder

AI Labs Utrecht University

EXDIGIT (Excellence in Digital Sciences and Interdisciplinary Technology

Land Salzburg

Publisher

Association for Computing Machinery (ACM)

Reference267 articles.

1. Multistakeholder recommendation: Survey and research directions

2. Himan Abdollahpouri, Edward C. Malthouse, Joseph A. Konstan, Bamshad Mobasher, and Jeremy Gilbert. 2021. Toward the next generation of news recommender systems. In Proceedings of the Web Conference (WWW’21). ACM, New York, NY, 402–406. DOI:10.1145/3442442.3452327

3. Panagiotis Adamopoulos. 2013. Beyond rating prediction accuracy: On new perspectives in recommender systems. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys’13). ACM, New York, NY, 459–462. DOI:10.1145/2507157.2508073

4. Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques

5. Shikha Agarwal and Archana Singhal. 2014. Handling skewed results in news recommendations by focused analysis of semantic user profiles. In Proceedings of the International Conference on Reliability Optimization and Information Technology (ICROIT’14). IEEE, 74–79. DOI:10.1109/icroit.2014.6798295

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3