Beyond Personalization: Research Directions in Multistakeholder Recommendation

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article describes the origins of multistakeholder recommendation, and the landscape of system designs. It provides illustrative examples of current research, as well as outlining open questions and research directions for the field.Comment: 64 page

Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison

Research on fairness in machine learning has been recently extended to recommender systems. One of the factors that may impact fairness is bias disparity, the degree to which a group's preferences on various item categories fail to be reflected in the recommendations they receive. In some cases biases in the original data may be amplified or reversed by the underlying recommendation algorithm. In this paper, we explore how different recommendation algorithms reflect the tradeoff between ranking quality and bias disparity. Our experiments include neighborhood-based, model-based, and trust-aware recommendation algorithms.Comment: Workshop on Recommendation in Multi-Stakeholder Environments (RMSE) at ACM RecSys 2019, Copenhagen, Denmar

Incorporating System-Level Objectives into Recommender Systems

One of the most essential parts of any recommender system is personalization-- how acceptable the recommendations are from the user's perspective. However, in many real-world applications, there are other stakeholders whose needs and interests should be taken into account. In this work, we define the problem of multistakeholder recommendation and we focus on finding algorithms for a special case where the recommender system itself is also a stakeholder. In addition, we will explore the idea of incremental incorporation of system-level objectives into recommender systems over time to tackle the existing problems in the optimization techniques which only look for optimizing the individual users' lists.Comment: arXiv admin note: text overlap with arXiv:1901.0755

Legal Mechanisms for Governing the Transition of Key Domain Name Functions to the Global Multi-Stakeholder Community

This Chapter proposes an alternative approach to the IANA transition that migrates the existing core contractual requirements imposed by the US government to the existing IANA functions customers. It also advances modest internal accountability revisions that could be undertaken within ICANN’s existing structure. Specifically, it advocates that the Independent Review Tribunal charged with reviewing certain ICANN board of directors-related decisions be selected by a multi-stakeholder committee rather than being subject to approval by ICANN and expanding the grounds for review to cover all of the rubrics recommended by ICANN’s “Improving Institutional Confidence” process in 2008-2009, including fairness, fidelity to the power, cogency of decision making and addressing the public interest. This new tribunal could be drawn from a standing panel of internationally recognized relevant technical experts, as well as internationally recognized jurists. Members of ICANN’s various stakeholder groups and the public should be able to make comments on the proposed bench before final appointment

A children’s rights perspective on the responsibility of social network site providers

It is the aim of this paper to analyse this issue from a children’s right perspective and to identify a theoretical, broader basis that can be used by policymakers to persuade social network site providers to enhance their Corporate Social Responsibility efforts to provide young users with a communication and interaction platform that respects and helps realising their fundamental rights.status: publishe

Recommender systems and their ethical challenges

This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review. The article identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the literature: currently user-centred approaches do not consider the interests of a variety of other stakeholders—as opposed to just the receivers of a recommendation—in assessing the ethical impacts of a recommender system