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

The Multisided Complexity of Fairness in Recommender Systems

Recommender systems are poised at the interface between stakeholders: for example, job applicants and employers in the case of recommendations of employment listings, or artists and listeners in the case of music recommendation. In such multisided platforms, recommender systems play a key role in enabling discovery of products and information at large scales. However, as they have become more and more pervasive in society, the equitable distribution of their benefits and harms have been increasingly under scrutiny, as is the case with machine learning generally. While recommender systems can exhibit many of the biases encountered in other machine learning settings, the intersection of personalization and multisidedness makes the question of fairness in recommender systems manifest itself quite differently. In this article, we discuss recent work in the area of multisided fairness in recommendation, starting with a brief introduction to core ideas in algorithmic fairness and multistakeholder recommendation. We describe techniques for measuring fairness and algorithmic approaches for enhancing fairness in recommendation outputs. We also discuss feedback and popularity effects that can lead to unfair recommendation outcomes. Finally, we introduce several promising directions for future research in this area

Mosaic: Designing Online Creative Communities for Sharing Works-in-Progress

Online creative communities allow creators to share their work with a large audience, maximizing opportunities to showcase their work and connect with fans and peers. However, sharing in-progress work can be technically and socially challenging in environments designed for sharing completed pieces. We propose an online creative community where sharing process, rather than showcasing outcomes, is the main method of sharing creative work. Based on this, we present Mosaic---an online community where illustrators share work-in-progress snapshots showing how an artwork was completed from start to finish. In an online deployment and observational study, artists used Mosaic as a vehicle for reflecting on how they can improve their own creative process, developed a social norm of detailed feedback, and became less apprehensive of sharing early versions of artwork. Through Mosaic, we argue that communities oriented around sharing creative process can create a collaborative environment that is beneficial for creative growth

Homeschool 101: Using Visual Media to Promote Awareness of Homeschooling

Parents who are making decisions about the education of their children can benefit from a general awareness of the full range of schooling options available to them. Although the United States has seen its homeschooling population grow to all-time highs in recent years, many people are still unfamiliar with what homeschooling is actually like in practice. Some parents fail to recognize homeschooling as a viable option because of they are unfamiliar with its potential benefits, standard homeschooling methods, and resources for homeschooling families. This thesis reviews the homeschooling research literature and overviews relevant case studies to inform a visual solution to this problem. I developed a multifaceted information campaign to demonstrate how graphic design and visual media can be used to advance awareness of homeschooling as a viable option for education. The campaign provides general information about the homeschool movement and presents a picture of what contemporary homeschooling looks like

Content Creation in the Digital Economy: A Comprehensive Exploration and Investigation of Work Environment and Content Creators’ Behaviours

With the emergence and rapid spread of digital technologies, the world is undergoing a profound transformation. The digital economy that has evolved as a result has fundamentally changed and impacted every aspect of society and business, and it will undoubtedly change and reshape employment and work from various perspectives as well. Flexibility and autonomy have always been the strong attraction that the digital economy provides to workers, but behind this hidden truth is the strict control of platforms and algorithms. This thesis seeks to further deepen the understanding of working in the digital economy through a series of studies ranging from the broad to the specific, especially on the work of a particular group of content creators. This thesis contains four studies. Study 1 is a review paper that attempts to clarify the distinction between different concepts from the digital economy on a macro level. Studies 2-4 turn the perspective to a particular group of workers in the digital economy, the content creators. Study 2 uses two quantitative studies to theorise the characteristics of working on content creative platforms by developing a typology of these platforms. The third study was a systematic review to explore the power imbalance between platform algorithms and creators in content creative platforms. The fourth study employs a quantitative study that explores the impact of the platform work environment on the creators' behaviour from an individual perspective. This series of studies makes important theoretical contributions to the field related to employment relations in the digital economy context, especially content creative platforms, from both macro and micro perspectives. In addition, this series of studies provides practical implications for content creators, platforms and policymakers

Controlling the Fairness / Accuracy Tradeoff in Recommender Systems

Recommender systems are one of the most pervasive applications of machine learning. They play a pivotal role in helping users find items tailored to their taste. Although these systems intend to assist people in their information needs, they can cause implicit or explicit discrimination against individuals or groups. There are several ways that different biases can creep into recommender systems. Reflection of societal and historical prejudices in datasets and during the data collection process, lack of sufficient data on minority groups, lack of suitable evaluation methods and model designs to detect these biases and lessen the unfairness caused by them are among the many reasons for unfairness in these systems. A system needs to defend against the biases in recommendation output to prevent harm and unfairness. However, integrating the goal of fairness with accuracy in recommender systems is challenging, primarily because of this goal's significant trade-offs with accuracy. Accuracy in recommender systems is the ability of that system to predict users' needs and interests accurately. On the other hand, fairness is a complicated concept with a variety of definitions. To use fairness as an objective, we need to define it based on the application area and the context of a problem. Additionally, we need to specify the fairness concerns of the different stakeholders involved in the recommender systems and the fairness priorities of a system. Any of these aspects might disagree with the goal of accuracy. For example, if fairness for content providers is more exposure to users, increasing it might cause a reduction in accuracy. Therefore, controlling the trade-off between accuracy and fairness becomes essential. Throughout this dissertation, several recommendation models and re-ranking approaches are presented that aim to address this problem using in- and post- processing methods. These approaches show promising results, but it is worth mentioning that they have intrinsic limitations and, therefore, shouldn't be considered ultimate solutions