Fairness in Recommendation: Foundations, Methods and Applications

As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision making. The satisfaction of users and the interests of platforms are closely related to the quality of the generated recommendation results. However, as a highly data-driven system, recommender system could be affected by data or algorithmic bias and thus generate unfair results, which could weaken the reliance of the systems. As a result, it is crucial to address the potential unfairness problems in recommendation settings. Recently, there has been growing attention on fairness considerations in recommender systems with more and more literature on approaches to promote fairness in recommendation. However, the studies are rather fragmented and lack a systematic organization, thus making it difficult to penetrate for new researchers to the domain. This motivates us to provide a systematic survey of existing works on fairness in recommendation. This survey focuses on the foundations for fairness in recommendation literature. It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking in order to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems. After that, the survey will introduce fairness in recommendation with a focus on the taxonomies of current fairness definitions, the typical techniques for improving fairness, as well as the datasets for fairness studies in recommendation. The survey also talks about the challenges and opportunities in fairness research with the hope of promoting the fair recommendation research area and beyond.Comment: Accepted by ACM Transactions on Intelligent Systems and Technology (TIST

Total Hotel Revenue Management: A Strategic Profit Perspective

Hospitality firms are expanding traditional revenue management (RM) practice to focus on customer value and strategic profit management. Participants in series of semi-structured interviews suggested that revenue management is moving away from a sole focus on top-line rooms revenue toward a bottom-line orientation focused on the customer. Thus, RM will expand to multiple revenue sources and encompass a multi-channel demand management approach. The interviews with sixteen senior hotel leaders, RM vendors, and solution providers highlighted the importance of profit, rather than just revenue, given rising distribution and variable costs. Despite the attraction of other revenue and profit sources, such as F&B, spas, and function space, the participants noted that expanding RM to those areas involves complexities not found in the rooms division. Ideally, hoteliers seek to assess the value of each customer’s patronage and develop a specific relationship with each customer. With changes envisioned by these hotel leaders, the practice of revenue management will evolve into the more accurate and expansive notion of strategic profit management

Conceptions of Corporate Purpose in Post-Crisis Financial Firms

American “populism” has had a major impact on the development of U.S. corporate governance throughout its history. Specifically, appeals to the perceived interests of average working people have exerted enormous social and political influence over prevailing conceptions of corporate purpose—that is, the aims toward which society expects corporate decision-making to be directed. In this Article, I assess the impact of American populism upon prevailing conceptions of corporate purpose, contrasting its unique expression in the context of financial firms with that arising in other contexts. I then examine its impact upon corporate governance reforms enacted in the wake of the financial and economic crisis that emerged in 2007. In Part II, I explore how populism has historically shaped conceptions of corporate purpose in the United States.In Part III, I turn to the crisis, arguing that growing shareholder centrism over recent decades goes a long way toward explaining excessive risk-taking in financial firms—a conclusion rendering post-crisis reforms aimed at further strengthening shareholders a surprising and alarming development. I conclude in Part IV that the potential corporate governance reforms most worthy of consideration include those aimed at accomplishing precisely the opposite: insulating financial-firm management from equity-market pressures and associated risk incentives

User-Generated Data Network Effects and Market Competition Dynamics

This Article defines User-Generated Data (“UGD”) network effects, distinguishes them from the more familiar concept of traditional network effects, and explores their implications for market competition dynamics. It explains that UGD network effects produce various efficiencies for digital service providers (“data platforms”) by empowering their services’ optimization, personalization, and continuous diversification. In light of these efficiencies, competition dynamics in UGD-driven markets tend to be unstable and lead to the formation of dominant multi-industry conglomerates. These processes will enhance social welfare because they are natural and efficient. Conversely, countervailing UGD network effects also empower data platforms to detect and neutralize competitive threats, price discriminate among users, and manipulate users’ behaviors. The realization of these effects will result in inefficiencies, which will undermine social welfare. After a comprehensive analysis of conflicting economic forces, this Article sets the ground for informed policymaking. It suggests that emerging calls to aggravate antitrust enforcement and to “break up” Big Tech are ill-advised. Instead, this Article calls for policymakers to draw inspiration from traditional network industries’ public utility and open-access regulations