克服推荐系统中的各种意外效应:义务论方法

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Paula G. Duran, Pere Gilabert, Santi Seguí, Jordi Vitrià
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引用次数: 0

摘要

在当今的数字时代,推荐系统作为一种引导用户使用个性化产品、服务和内容的手段,已经变得无处不在。然而,尽管推荐系统得到了广泛应用,研究成果也源远流长,但这些系统也难免存在缺陷。推荐系统面临的一个重大挑战是存在偏差,这会产生各种不良影响,其中最突出的是人气偏差。这种偏差妨碍了推荐项目的多样性,从而限制了用户接触不那么受欢迎或小众的内容。此外,如果考虑到多个利益相关者,这个问题就会变得更加复杂,需要平衡多个可能相互冲突的目标。在本文中,我们提出了一种新的方法来解决涉及不同利益相关者的推荐系统中的各种不期望后果。我们没有采用旨在减轻推荐政策影响的结果论观点,而是提出了一种以一套最基本的道德原则为中心的义务论方法。更确切地说,我们提出了两个不同的原则,旨在避免对预测过于自信,并准确地模拟用户的真正利益。所提出的方法避免了定义多目标系统的需要,而多目标系统是开发复杂推荐器的主要限制之一。通过广泛的实验,我们展示了我们的方法在从用户和项目两个角度减轻推荐器的不利影响方面的功效,最终提高了各种超越准确性的指标。这项研究强调了负责任的公平推荐的重要性,并提出了一种可以在现实世界中轻松部署的策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Overcoming Diverse Undesired Effects in Recommender Systems: A Deontological Approach

In today’s digital landscape, recommender systems have gained ubiquity as a means of directing users towards personalized products, services, and content. However, despite their widespread adoption and a long track of research, these systems are not immune to shortcomings. A significant challenge faced by recommender systems is the presence of biases, which produces various undesirable effects, prominently the popularity bias. This bias hampers the diversity of recommended items, thus restricting users’ exposure to less popular or niche content. Furthermore, this issue is compounded when multiple stakeholders are considered, requiring the balance of multiple, potentially conflicting objectives.

In this paper, we present a new approach to address a wide range of undesired consequences in recommender systems that involve various stakeholders. Instead of adopting a consequentialist perspective that aims to mitigate the repercussions of a recommendation policy, we propose a deontological approach centered around a minimal set of ethical principles. More precisely, we introduce two distinct principles aimed at avoiding overconfidence in predictions and accurately modeling the genuine interests of users. The proposed approach circumvents the need for defining a multi-objective system, which has been identified as one of the main limitations when developing complex recommenders. Through extensive experimentation, we show the efficacy of our approach in mitigating the adverse impact of the recommender from both user and item perspectives, ultimately enhancing various beyond accuracy metrics. This study underscores the significance of responsible and equitable recommendations and proposes a strategy that can be easily deployed in real-world scenarios.

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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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