PrCP: Pre-recommendation Counter-Polarization.
Mahsa Badami,Olfa Nasraoui,Patrick Shafto +2 more
- pp 280-287
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This article is published in International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management.The article was published on 2018-01-01 and is currently open access. It has received 10 citations till now.read more
Citations
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Proceedings ArticleDOI
Understanding Echo Chambers in E-commerce Recommender Systems
TL;DR: The echo chamber phenomenon in Alibaba Taobao --- one of the largest e-commerce platforms in the world --- is analyzed and evidence suggests the tendency of echo chamber in user click behaviors, while it is relatively mitigated in user purchase behaviors.
Proceedings ArticleDOI
Understanding Echo Chambers in E-commerce Recommender Systems
TL;DR: Zhang et al. as discussed by the authors analyzed the echo chamber phenomenon in Alibaba Taobao and found that the effect of user interests being reinforced through repeated exposure to similar contents can lead to the self-reinforcing of user's interests.
Journal ArticleDOI
Evolution and impact of bias in human and machine learning algorithm interaction
TL;DR: It is argued that algorithmic bias interacts with humans in an iterative manner, which has a long-term effect on algorithms’ performance, and three different iterated bias modes, as well as initial training data class imbalance and human action, do affect the models learned by machine learning algorithms.
Proceedings ArticleDOI
The Dual Echo Chamber: Modeling Social Media Polarization for Interventional Recommending
Tim Donkers,Jürgen Ziegler +1 more
TL;DR: In this article, the authors argue that echo chambers are social phenomena that amplify agreement and suppress opposing views in social media which may lead to fragmentation and polarization of the user population, and they use knowledge graph embedding techniques to generate recommendations and utilize logical graph queries in embedding spaces to diversify recommendations aimed at challenging polarization in online discussions.
Proceedings ArticleDOI
Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System
TL;DR: A theoretical framework is presented to model the asymptotic evolution of the different components of a recommender system operating within a feedback loop setting, and derive theoretical bounds and convergence properties on quantifiable measures of the user discovery and blind spots.
References
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Journal ArticleDOI
Matrix Factorization Techniques for Recommender Systems
TL;DR: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
Journal ArticleDOI
Biased Assimilation and Attitude Polarization: The Effects of Prior Theories on Subsequently Considered Evidence
TL;DR: In this paper, subjects supporting and opposing capital punishment were exposed to two purported studies, one seemingly confirming and one seemingly disconfirming their existing beliefs about the deterrent efficacy of the death penalty.
Proceedings Article
Equality of opportunity in supervised learning
TL;DR: This work proposes a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features and shows how to optimally adjust any learned predictor so as to remove discrimination according to this definition.
Journal ArticleDOI
Semantics derived automatically from language corpora contain human-like biases
TL;DR: This article showed that applying machine learning to ordinary human language results in human-like semantic biases and replicated a spectrum of known biases, as measured by the Implicit Association Test, using a widely used, purely statistical machine-learning model trained on a standard corpus of text from the World Wide Web.
Journal ArticleDOI
Group polarization: A critical review and meta-analysis.
TL;DR: The authors review group polarization studies that address themselves to either one of the two primary explanatory mechanisms thought to underly group polarization, namely social comparison and persuasive argumentation processes, and present a summary of the effect sizes of these studies.