Practitioners Versus Users: A Value-Sensitive Evaluation of Current Industrial Recommender System Design

Abstract

Recommender systems are playing an increasingly important role in alleviating information overload and supporting users' various needs, e.g., consumption, socialization, and entertainment. However, limited research focuses on how values should be extensively considered in industrial deployments of recommender systems, the ignorance of which can be problematic. To fill this gap, in this paper, we adopt Value Sensitive Design to comprehensively explore how practitioners and users recognize different values of current industrial recommender systems. Based on conceptual and empirical investigations, we focus on five values: recommendation quality, privacy, transparency, fairness, and trustworthiness. We further conduct in-depth qualitative interviews with 20 users and 10 practitioners to delve into their opinions about these values. Our results reveal the existence and sources of tensions between practitioners and users in terms of value interpretation, evaluation, and practice, which provide novel implications for designing more human-centric and value-sensitive recommender systems.

Zhilong Chen and Jinghua Piao contribute equally to this work; Accepted to CSCW 2022

References in corpus (6)

• DGCN: Diversified Recommendation with Graph Convolutional Networks
• Fairness and Transparency in Recommendation: The Users' Perspective
• Modeling Assumptions Clash with the Real World: Transparency, Equity, and Community Challenges for Student Assignment Algorithms
• Content-aware Neural Hashing for Cold-start Recommendation
• Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste
• Practitioners Versus Users: A Value-Sensitive Evaluation of Current Industrial Recommender System Design

Cited by in corpus (1)

• Practitioners Versus Users: A Value-Sensitive Evaluation of Current Industrial Recommender System Design