Group recommendation systems (GRS) provide personalized recommendations to multiple users. While GRS has garnered attention as a social catalyst that can foster social interactions among users, how to design GRS to effectively support such experiences remains underexplored. To address this gap, we conducted an empirical study of a YouTube video GRS involving 23 participants across 8 groups over 16 days. Using experience prototyping combined with a diary study and follow-up interviews, we investigated users’ perceptions of three design attributes of GRS: algorithmic controllability, algorithmic transparency, and behavioral visibility. Our findings reveal the social experiences enabled by these design attributes, as well as the challenges and concerns that emerged. Based on these findings, we conceptualize five roles of GRS as a social catalyst (i.e., Hinter, Connector, Probe, Homing Pigeon, and Provocateur ) and suggest design considerations for leveraging GRS as a novel design material to facilitate meaningful social interactions.

On-demand ride-pooling (ODRP) services are a new alternative modes of transportation that have recently emerged due to technological advancements.While algorithmic management plays a crucial role in ODRP services and can create complex workplace dynamics, the experiences of ODRP workers remain underexplored in the HCI field.To address this gap, we interviewed 16 drivers of Shucle, an ODRP service in South Korea.We examined the drivers' detailed work practices, focusing on the perceived challenges of working under algorithmic management and the perceived benefits and necessity of algorithmic management.This paper provides empirical evidence of the impact of algorithmic management on ODRP drivers' work environments and discusses the implications of our findings for supporting algorithmic workplaces in ODRP services.By positioning ODRP drivers as company employees embedded within a vast, dynamic traffic environment, our study extends algorithmic management scholarship beyond gig work and other algorithmic work contexts, offering fresh insights into how autonomy and accountability are configured across algorithmic workplaces.

The reliability of online dating algorithms has sparked considerable debate, particularly regarding skepticism about their excessive emphasis on evaluating and getting evaluated, which often overshadows the quest for authentic romantic connections. To understand the multifaceted influence of dating algorithms on end-users and explore avenues for algorithmic features considering the dynamics of human relationships, we conducted a mixed-methods study comprising in-depth interviews (N = 22) and a metaphoric co-design workshop (N = 12) with active users of online dating platforms. Interviews revealed that users perceive and respond to algorithmic evaluations with varied perceptions and behaviors, often expressing concerns about the emotional burden of constant self-presentation and the pursuit of quantitative assessments over genuine connections. In the design workshop, users envisioned desired algorithmic features to overcome investigated challenges, such as prioritizing personal values, tailored matchmaking, and support for personal growth in relationships. This research contributes to unraveling the complex dynamics of human-algorithm interaction in the context of online dating. By aligning algorithmic functions more closely with user desires and relationship goals, this study paves the way for more meaningful and authentic connections in the digital dating landscape.