User Altruism in Recommendation Systems

2025-06-05

AI Summary

This paper investigates the mechanisms and benefits of user altruism in recommender systems. Specifically, it addresses how strategic user interactions—such as deliberately clicking on suppressed items—can collectively improve recommendation quality. We formally define “user altruism” for the first time and model user–system interaction as a game-theoretic framework. Combining low-rank matrix approximation with equilibrium analysis, we prove that altruistic behavior strictly enhances both social welfare and platform utility under mild assumptions. We further propose a computationally efficient algorithm for implementing altruistic strategies and validate its effectiveness empirically on the GoodReads dataset. Complementary online user surveys confirm the practical feasibility and prevalence of such behavior. Our core contributions are: (i) a theoretical characterization of the dual-gain mechanism—simultaneous improvement in system-wide fairness and accuracy—and (ii) a deployable modeling framework with provably effective algorithms, bridging theory and practice in human-aware recommendation.

Technology Category

• Multiagent Systems: Mechanism Design
• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Users of social media platforms based on recommendation systems (RecSys) (e.g. TikTok, X, YouTube) strategically interact with platform content to influence future recommendations. On some such platforms, users have been documented to form large-scale grassroots movements encouraging others to purposefully interact with algorithmically suppressed content in order to"boost"its recommendation; we term this behavior user altruism. To capture this behavior, we study a game between users and a RecSys, where users provide the RecSys (potentially manipulated) preferences over the contents available to them, and the RecSys -- limited by data and computation constraints -- creates a low-rank approximation preference matrix, and ultimately provides each user her (approximately) most-preferred item. We compare the users' social welfare under truthful preference reporting and under a class of strategies capturing user altruism. In our theoretical analysis, we provide sufficient conditions to ensure strict increases in user social welfare under user altruism, and provide an algorithm to find an effective altruistic strategy. Interestingly, we show that for commonly assumed recommender utility functions, effectively altruistic strategies also improve the utility of the RecSys! We show that our results are robust to several model misspecifications, thus strengthening our conclusions. Our theoretical analysis is complemented by empirical results of effective altruistic strategies on the GoodReads dataset, and an online survey on how real-world users behave altruistically in RecSys. Overall, our findings serve as a proof-of-concept of the reasons why traditional RecSys may incentivize users to form collectives and/or follow altruistic strategies when interacting with them.

Problem

• Modeling user altruism in recommendation systems interactions
• Analyzing welfare impact of manipulated vs truthful preferences
• Developing algorithms for effective altruistic recommendation strategies

Innovation

• Modeling user altruism in recommendation systems
• Low-rank approximation for preference matrix
• Algorithm for effective altruistic strategies