Abstract. As mobile devices are always with users and music listening is a very personal and situational behaviour, contextual information could be used to greatly enhance music recommendations. However, making such use of context, while learning user profiles, is still a challenging problem. We present a system for collecting context and usage data from mobile devices, but targeted at recommending music according to learned user profiles and specific situations. The developed data flow system requires supporting both short enough response times and longer asynchronous reasoning on the collected data. Furthermore, the mobile phone acts not only as sensor, but is directly related to the effectiveness of the music service experience. Thus, this paper provides a description of our approach to the system and the initial results of a usability test of the mobile application and its backend system.

Figure 1. Current playing song screen and recommendations carrousel. AbstractMusic listening is a very personal and situational behaviour, which suggests that contextual information could be used to greatly enhance music recommendation experience. However, making such use of mobile context, while learning user profiles, is a challenging problem. This case study presents a system for collecting context and usage data from mobile devices, but targeted at recommending music via unsupervised learning of user profiles and relevant situations. The developed data flow system supports both short enough response times and longer asynchronous reasoning on the collected data; furthermore, the mobile phone acts not only as sensor, but the mobile app is directly tied to the effectiveness of the music service user experience (UX). This work describes our system design and discusses issues related to the problem space and to usability tests on such systems, based on an international user trial. CR Categories: • Information systems~Recommender systems • Human-centered computing~Empirical studies in ubiquitous and mobile computing

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