The advancement of large language models (LLMs) now allows users to actively interact with conversational recommendation systems (CRS) and build their own personalized recommendation services tailored to their unique needs and goals. This experience offers users a significantly higher level of controllability compared to traditional RS, enabling an entirely new dimension of recommendation experiences. Building on this context, this study explored the unique experiences that LLM-powered CRS can provide compared to traditional RS. Through a three-week diary study with 12 participants using custom GPTs for music recommendations, we found that LLM-powered CRS can (1) help users clarify implicit needs, (2) support unique exploration, and (3) facilitate a deeper understanding of musical preferences. Based on these findings, we discuss the new design space enabled by LLM-powered CRS and highlight its potential to support more personalized, user-driven recommendation experiences.

Sensors and actuators are crucial components of a do-it-yourself (DIY) smart home system that enables users to construct smart home features successfully. In addition, machine learning (ML) (e.g., ML-intensive camera sensors) can be applied to sensor technology to increase its accuracy. Although camera sensors are often utilized in homes, research on user experiences with DIY smart home systems employing camera sensors is still in its infancy. This research investigates novel user experiences while constructing DIY smart home features using an ML-intensive camera sensor in contrast to commonly used IoT sensors. Thus, we conducted a seven-day field diary study with 12 families who were given a DIY smart home kit. Here, we assess the five characteristics of the camera sensor as well as the potential and challenges of utilizing the camera sensor in the DIY smart home and discuss the opportunities to address existing DIY smart home issues.