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.

Designing user experiences for group recommendation systems (GRS) is challenging, requiring a nuanced understanding of the influence of social interactions between users. Using Spotify Blend as a real-world case of music GRS, we conducted empirical studies to investigate intricate social interactions among South Korean users in GRS. Through a preliminary survey about Blend experiences in general, we narrowed the focus for the main study to relationships between two users who are acquainted or close. Building on this, we conducted a 21-day diary study and interviews with 30 participants (15 pairs) to probe more in-depth interpersonal dynamics within Blend. Our findings reveal that users engaged in implicit social interactions, including tacit understanding of their companions and indirect communication. We conclude by discussing the newly discovered value of GRS as a social catalyst, along with design attributes and challenges for the social experiences it mediates.

Nowadays, many mobile instant messenger (MIM) users employ stickers to express their emotions. Due to the increase in the variety of stickers, many MIM platforms have begun to offer sticker recommendation services, which suggest appropriate stickers based on the context of conversations. Although sticker recommendation services need to consider users' subtle social and psychological needs from the perspective that stickers are an alternative communication language, few attempts have been made to explore this area. Consequently, we investigated sticker usage practices through a qualitative study with 18 KakaoTalk users in South Korea. Based on the results of this study, we revealed user's three common considerations in sticker usage experiences and discussed the design implications for sticker recommendation services in MIM.

The number of people with vision impairments using Conversational Agents (CAs) has increased because of the potential of this technology to support them. As many visually impaired people are accustomed to understanding fast speech, most screen readers or voice assistant systems offer speech rate settings. However, current CAs are designed to interact at a human-like speech rate without considering their accessibility. In this study, we tried to understand how people with vision impairments use CA at a fast speech rate. We conducted a 20-day in-home study that examined the CA use of 10 visually impaired people at default and fast speech rates. We investigated the difference in visually impaired people's CA use with different speech rates and their perception toward CA at each rate. Based on these findings, we suggest considerations for the future design of CA speech rate for those with visual impairments.