减少极化的文章推荐优化模型

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI:10.1145/3487351.3488349

Inzamam Rahaman, Patrick Hosein

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引用次数: 2

摘要

在线社交网络被指控加强和扩大了社会的两极分化。这种两极分化会对个人和整个社会的健康产生负面影响。因此，至关重要的是，我们的在线社交网络由避免两极分化并寻求遏制两极分化的算法驱动。其中一个目标将是向在线社交网络用户提供的精心策划的新闻推送。在本文中，我们提出了一个随机动态规划(SDP)模型，旨在向用户推荐文章，同时保持他们的订阅，同时减少两极分化。我们还提出了启发式近似的解决方案，以及与这些启发式的最优经验比较。我们发现，一般来说，在我们的SDP模型下，推荐倾向于中立的文章对减少两极分化具有积极作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A model for optimizing article recommendation for reducing polarization

Online social networks have been charged with enhancing and augmenting polarization in society. This polarization can have negative repercussions on the health of both individuals and society on the whole. Hence, it is vital that our online social networks are powered by algorithms that avoid polarisation and seek to curtail it. One target would be the curated news feed supplied to users in online social networks. In this paper, we present a stochastic dynamic programming (SDP) model that seeks to recommend articles to users with the aim of simultaneously retaining their subscription whilst reducing polarization. We also present heuristics to approximate the solution alongside an empirical comparison of the optimal vis-a-vis these heuristics. We find that, in general, recommending articles that tend towards neutrality has a positive effect in reducing polarization under our SDP model.

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来源期刊

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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