The Stereotyping Problem in Collaboratively Filtered Recommender Systems

Guo, Wenshuo; Krauth, Karl; Jordan, Michael I.; Garg, Nikhil

doi:10.1145/3465416.3483298

Cited by 10 publications

(7 citation statements)

References 32 publications

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“…In sequential settings, this may be trickier since an item's value may depend on future recommendations, and may influence future recommendations that user receives. This requires the user be sequentially rational (i.e., plan)-but not strategic-and possibly invoke a "mental model" of the RS policy to explicitly influence subsequent recommendations (Guo et al 2021); e.g., a user selecting a (non-preferred) music track by some artist to induce future recommendations of (preferred) tracks by that artist.…”

Section: Strategic Behaviormentioning

confidence: 99%

Recommender Ecosystems: A Mechanism Design Perspective on Holistic Modeling and Optimization

Boutilier,

Mladenov,

Tennenholtz

2024

AAAI

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Modern recommender systems lie at the heart of complex recommender ecosystems that couple the behavior of users, content providers, vendors, advertisers, and other actors. Despite this, the focus of much recommender systems research and deployment is on the local, myopic optimization of the recommendations made to individual users. This comes at a significant cost to the long-term utility that recommender systems generate for their users. We argue that modeling the incentives and behaviors of these actors, and the interactions among them induced by the recommender systems, is needed to maximize value and improve overall ecosystem health. Moreover, we propose the use of economic mechanism design, an area largely overlooked in recommender systems research, as a framework for developing such models. That said, one cannot apply “vanilla” mechanism design to recommender ecosystem modeling optimization out of the box—the use of mechanism design raises a number of subtle and interesting research challenges. We outline a number of these in this talk (and paper), emphasizing the need to develop nonstandard approaches to mechanism design that intersect with numerous areas of research, including preference modeling, reinforcement learning and exploration, behavioral economics, and generative AI, among others.

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Section: Strategic Behaviormentioning

confidence: 99%

Recommender Ecosystems: A Mechanism Design Perspective on Holistic Modeling and Optimization

Boutilier,

Mladenov,

Tennenholtz

2024

AAAI

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“…Recently, the RS community argued for the importance of looking beyond accuracy [6] in RS-evaluation. Design-wise, there is an ongoing struggle to develop diverse RSs [7][8][9] which, moreover, ensure that any two items could be recommended jointly to users [10]. This perhaps explains why the popularity-based algorithms implemented within the RS community [12,21] differ from PA [4,14] by not allowing for a complete exploration of alternatives.…”

Section: Related Workmentioning

confidence: 99%

“…Recent literature in RSs argued why and how we should encourage diversity by finding the right balance between exploration and exploitation [6][7][8][9]. In practice, even pairs of popular items might not be jointly accessible to users, i.e., if a user is recommended and follows one of the two, they will not be recommended the other [10]. This puts real-world RS in stark contrast with PA and uniform random (UR) recommendations where every user can be recommended any CC.…”

Section: Introductionmentioning

confidence: 99%

Individual Fairness for Social Media Influencers

Ionescu,

Pagan,

Hannak

2023

Preprint

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Nowadays, many social media platforms are centered around content creators (CC). On these platforms, the tie formation process depends on two factors: (a) the exposure of users to CCs (decided by, e.g., a recommender system), and (b) the following decision-making process of users. Recent research studies underlined the importance of content quality by showing that under exploratory recommendation strategies, the network eventually converges to a state where the higher the quality of the CC, the higher their expected number of followers. In this paper, we extend prior work by (a) looking beyond averages to assess the fairness of the process and (b) investigating the importance of exploratory recommendations for achieving fair outcomes. Using an analytical approach, we show that non-exploratory recommendations converge fast but usually lead to unfair outcomes. Moreover, even with exploration, we are only guaranteed fair outcomes for the highest (and lowest) quality CCs.

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“…Although user reactions can serve as a proxy, albeit imperfect, for user preferences where ground truth about these preferences is not available, the use of such proxies in training recommendation algorithms can also have undesirable effects. Prior research studies demonstrate that recommender algorithms' suggestions correlate with user radicalization [100], discriminate against users and social groups [56], and replicate political bias in discussions [66,95]. However, only few studies bridge between political discourse theories, design, and engineering [59,83] to produce a better understanding of these phenomena.…”

Section: 33mentioning

confidence: 99%

Upvotes? Downvotes? No Votes? Understanding the relationship between reaction mechanisms and political discourse on Reddit

Papakyriakopoulos,

Engelmann,

Winecoff

2023

Preprint

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A significant share of political discourse occurs online on social media platforms. Policymakers and researchers try to understand the role of social media design in shaping the quality of political discourse around the globe. In the past decades, scholarship on political discourse theory has produced distinct characteristics of different types of prominent political rhetoric such as deliberative, civic, or demagogic discourse. This study investigates the relationship between social media reaction mechanisms (i.e., upvotes, downvotes) and political rhetoric in user discussions by engaging in an in-depth conceptual analysis of political discourse theory. First, we analyze 155 million user comments in 55 political subforums on Reddit between 2010 and 2018 to explore whether users' style of political discussion aligns with the essential components of deliberative, civic, and demagogic discourse. Second, we perform a quantitative study that combines confirmatory factor analysis with difference in differences models to explore whether different reaction mechanism schemes (e.g., upvotes only, upvotes and downvotes, no reaction mechanisms) correspond with political user discussion that is more or less characteristic of deliberative, civic, or demagogic discourse. We produce three main takeaways. First, despite being "ideal constructs of political rhetoric," we find that political discourse theories describe political discussions on Reddit to a large extent. Second, we find that discussions in subforums with only upvotes, or both up-and downvotes are associated with user discourse that is more deliberate and civic. Third, and perhaps most strikingly, social media discussions are most demagogic in subreddits with no reaction mechanisms at all. These findings offer valuable contributions for ongoing policy discussions on the relationship between social media interface design and respectful political discussion among users. 1

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The Stereotyping Problem in Collaboratively Filtered Recommender Systems

Cited by 10 publications

References 32 publications

Recommender Ecosystems: A Mechanism Design Perspective on Holistic Modeling and Optimization

Recommender Ecosystems: A Mechanism Design Perspective on Holistic Modeling and Optimization

Individual Fairness for Social Media Influencers

Upvotes? Downvotes? No Votes? Understanding the relationship between reaction mechanisms and political discourse on Reddit

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