Support the Underground: Characteristics of Beyond-Mainstream Music Listeners

Music recommender systems have become an integral part of music streaming services such as Spotify and Last.fm to assist users navigating the extensive music collections offered by them. However, while music listeners interested in mainstream music are traditionally served well by music recommender systems, users interested in music beyond the mainstream (i.e., non-popular music) rarely receive relevant recommendations. In this paper, we study the characteristics of beyond-mainstream music and music listeners and analyze to what extent these characteristics impact the quality of music recommendations provided. Therefore, we create a novel dataset consisting of Last.fm listening histories of several thousand beyond-mainstream music listeners, which we enrich with additional metadata describing music tracks and music listeners. Our analysis of this dataset shows four subgroups within the group of beyond-mainstream music listeners that differ not only with respect to their preferred music but also with their demographic characteristics. Furthermore, we evaluate the quality of music recommendations that these subgroups are provided with four different recommendation algorithms where we find significant differences between the groups. Specifically, our results show a positive correlation between a subgroup's openness towards music listened to by members of other subgroups and recommendation accuracy. We believe that our findings provide valuable insights for developing improved user models and recommendation approaches to better serve beyond-mainstream music listeners.Comment: Accepted for publication in EPJ Data Science - link to published version will be adde

Augmenting Learning Activities with Contextual Information Scent

Students often have information needs while carrying out a multitude of learning activities at universities. When information is needed for investigating a problem, the student may interrupt the work and switch to an information seeking task. As Internet connectivity becomes ubiquitous, searching information has been routinized and integrated in the learning experience. However, information needs are not always fully recognized, or they can not be well articulated. A MOOC student may perceive a video to be difficult, but fails to express what information can be helpful. Sometimes it is improper to interrupt the learning task for searching information, especially when social factors are concerned, e.g. in a seminar talk. These situations create research potentials for making ambient information cues, hereafter referred to as contextual information scent (CIS), available to address students' situational information needs in learning activities. The CIS is designed to combine context-awareness with information seeking, ambient interaction as well as serendipitous encounter. In this thesis, we investigate the CIS mainly in collaborative learning activities. We explore three different contexts: conversation, groupware interaction and video content for MOOC learning. RaindropSearch investigates capturing conversational words as CIS for building search queries, while the TileSearch triggers Web searches based on group discussions and retrieved image and Wikipedia results as CIS for serendipitous interactions. These two explorations both focus on conversation context and provide initial insights into the CIS design practice. Next, we present MeetHub Search, which includes three CIS components based on text interactions in a groupware. Our last prototype, the BOOC Player employs textbook pages as CIS and links them to MOOC videos during the course of collaborative video viewing. All prototypes show how we manipulated design parameters to reduce distraction, increase relevance and ensure timeliness. The studies also exhibit the influence of group dynamics on the use of CIS. We finally extend our research scope to individual MOOC learning and summarize the design insights obtained from MOOC analytics. The contributions of this thesis are summarized as (1) a dedicated research framework derived from both research literature and requirement analysis for recognizing the design challenges, design principles and design space of CIS. The framework lays the foundation for us to explore different contexts in this thesis, where we generated (2) design implications that identify the key attributes of CIS. Last but not least, we employed (3) a variety of evaluation methodologies in this thesis for assessing the usability as well as the benefit and appeal of CIS