How to Design More Empathetic Recommender Systems in Social Media

Titelangaben

Bonenberger, Lukas ; Graf-Drasch, Valerie ; Lanzl, Julia:
How to Design More Empathetic Recommender Systems in Social Media.
In: Proceedings of the 43rd International Conference on Information Systems (ICIS). - Copenhagen, Denmark , 2022

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Abstract

Social media’s value proposition heavily relies on recommender systems suggesting products to buy, events to attend, or people to connect with. These systems currently prioritize user engagement and social media providers’ profit generation over individual users’ well-being. However, making these systems more “empathetic” would benefit social media providers and content creators as users would use social media more often, longer, and increasingly recommend it to other users. By way of a design science research approach, including twelve interviews with psychologists, system designers, social media experts, and users, we develop user-centric design knowledge on making recommender systems in social media more “empathetic.” This design knowledge comprises a conceptual framework, four meta-requirements, and six design principles. It contributes to the evolving research stream “IS for resilience” and provides practical guidance in developing socially responsible recommender systems as next-generation social media services.