Recommender systems provide personalized information access to users of Internet services from social networks to e-commerce to media and entertainment. As is appropriate for research in a field with a focus on personalization, academic studies of recommender systems have largely concentrated on optimizing for user experience when designing, implementing and evaluating their algorithms and systems. However, this concentration on the user has meant that the field has lacked a systematic exploration of other aspects of recommender system outcomes. A user-centric approach limits the ability to incorporate system objectives, such as fairness, balance, and profitability, and obscures concerns that might come from other stakeholders, such as the providers or sellers of items being recommended. 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 outlines the multistakeholder perspective on recommendation, highlighting example research areas and discussing important issues, open questions, and prospective research directions.

中文翻译：

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多利益相关方建议：调查和研究方向

推荐系统为从社交网络到电子商务再到媒体和娱乐的互联网服务用户提供个性化信息访问。由于适合专注于个性化领域的研究，推荐系统的学术研究主要集中在设计、实施和评估其算法和系统时优化用户体验。然而，这种对用户的关注意味着该领域缺乏对推荐系统结果其他方面的系统探索。以用户为中心的方法限制了整合系统目标的能力，例如公平性、平衡性和盈利性，并掩盖了可能来自其他利益相关者的担忧，例如被推荐项目的供应商或销售商。多利益相关者推荐已成为描述和理解推荐设置的统一框架，其中最终用户不是唯一关注的焦点。本文概述了多利益相关方对推荐的看法，突出了示例研究领域，并讨论了重要问题、开放性问题和前瞻性研究方向。