Popularity Bias in Recommendation: A Multi-stakeholder Perspective

Traditionally, especially in academic research in recommender systems, the focus has been solely on the satisfaction of the end-user. While user satisfaction has, indeed, been associated with the success of the business, it is not the only factor. In many recommendation domains, there are other stakeholders whose needs should be taken into account in the recommendation generation and evaluation. In this dissertation, I describe the notion of multi-stakeholder recommendation. In particular, I study one of the most important challenges in recommendation research, popularity bias, from a multi-stakeholder perspective since, as I show later in this dissertation, it impacts different stakeholders in a recommender system. Popularity bias is a well-known phenomenon in recommender systems where popular items are recommended even more frequently than their popularity would warrant, amplifying long-tail effects already present in many recommendation domains. Prior research has examined various approaches for mitigating popularity bias and enhancing the recommendation of long-tail items overall. The effectiveness of these approaches, however, has not been assessed in multi-stakeholder environments. In this dissertation, I study the impact of popularity bias in recommender systems from a multi-stakeholder perspective. In addition, I propose several algorithms each approaching the popularity bias mitigation from a different angle and compare their performances using several metrics with some other state-of-the-art approaches in the literature. I show that, often, the standard evaluation measures of popularity bias mitigation in the literature do not reflect the real picture of an algorithm's performance when it is evaluated from a multi-stakeholder point of view.Comment: PhD Dissertation in Information Science (University of Colorado Boulder

From Psychology to Phylogeny: Bridging Levels of Analysis in Cultural Evolution

Cultural evolution, or change in the socially learned behavior of a population over time, is a fascinating phenomenon that is widespread in humans and present in some non-human animals. In this dissertation, I present an array of cultural evolutionary studies that bridge pattern and process in a wide range of research models including music, extremism, and birdsong. The first chapter is an introduction to the field of cultural evolution, including a bibliometric analysis of its structure. The second and third chapters are studies on the cultural dynamics of music sampling traditions in hip-hop and electronic music communities and far-right extremism in the United States, using social network analysis and epidemiological modeling, respectively. The fourth and fifth chapters are studies on how cultural transmission biases influence population-level changes in music sampling traditions and house finch song, using a combination of agent-based modeling and machine learning. The sixth chapter is a technical report on computerized birdfeeders that were used to remotely collect data on the social network structure of a wild house finch population. Lastly, the seventh chapter applies a novel phylogenetic method based on dynamic community detection to reconstruct the cultural evolution of electronic music