Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content external link

Reuver, M., Mattis, N., Sax, M., Verberne, S., Tintarev, N., Helberger, N., Müller, J., Vrijenhoek, S., Fokkens, A. & Van Atteveldt, W.
The 1st Workshop on NLP for Positive Impact: NLP4PosImpact 2021 : proceedings of the workshop, pp: 47-59, 2021

algorithmic news recommenders, diversity, diversity metrics

Bibtex

Article{Reuver2021, title = {Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content}, author = {Reuver, M. and Mattis, N. and Sax, M. and Verberne, S. and Tintarev, N. and Helberger, N. and Müller, J. and Vrijenhoek, S. and Fokkens, A. and Van Atteveldt, W.}, url = {https://aclanthology.org/2021.nlp4posimpact-1.6/}, doi = {https://doi.org/https://doi.org/10.18653/v1/2021.nlp4posimpact-1.6}, year = {0801}, date = {2021-08-01}, journal = {The 1st Workshop on NLP for Positive Impact: NLP4PosImpact 2021 : proceedings of the workshop}, keywords = {algorithmic news recommenders, diversity, diversity metrics}, }

Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity external link

Möller, J., Trilling, D., Helberger, N. & Es, B. van
Information, Communication & Society, 2018

Abstract

In the debate about filter bubbles caused by algorithmic news recommendation, the conceptualization of the two core concepts in this debate, diversity and algorithms, has received little attention in social scientific research. This paper examines the effect of multiple recommender systems on different diversity dimensions. To this end, it maps different values that diversity can serve, and a respective set of criteria that characterizes a diverse information offer in this particular conception of diversity. We make use of a data set of simulated article recommendations based on actual content of one of the major Dutch broadsheet newspapers and its users (N=21,973 articles, N=500 users). We find that all of the recommendation logics under study proved to lead to a rather diverse set of recommendations that are on par with human editors and that basing recommendations on user histories can substantially increase topic diversity within a recommendation set.

algoritmes, automated content classification, diversity metrics, filter bubbles, frontpage, news, recommender systems

Bibtex

Article{Möller2018, title = {Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity}, author = {Möller, J. and Trilling, D. and Helberger, N. and Es, B. van}, url = {https://www.ivir.nl/publicaties/download/ICS_2018.pdf}, doi = {https://doi.org/https://doi.org/10.1080/1369118X.2018.1444076}, year = {0308}, date = {2018-03-08}, journal = {Information, Communication & Society}, abstract = {In the debate about filter bubbles caused by algorithmic news recommendation, the conceptualization of the two core concepts in this debate, diversity and algorithms, has received little attention in social scientific research. This paper examines the effect of multiple recommender systems on different diversity dimensions. To this end, it maps different values that diversity can serve, and a respective set of criteria that characterizes a diverse information offer in this particular conception of diversity. We make use of a data set of simulated article recommendations based on actual content of one of the major Dutch broadsheet newspapers and its users (N=21,973 articles, N=500 users). We find that all of the recommendation logics under study proved to lead to a rather diverse set of recommendations that are on par with human editors and that basing recommendations on user histories can substantially increase topic diversity within a recommendation set.}, keywords = {algoritmes, automated content classification, diversity metrics, filter bubbles, frontpage, news, recommender systems}, }