Interactive exploration of music listening histories

Abstract

Over the past years, music listening histories have become easily accessible due to the expansion of online lifelogging services. These histories represent the sequence of songs listen by users over time. Although this data contains intrinsic users' tastes and listening behaviors, it has been mainly used to personalize recommendations. Tools to help users exploring and reasoning about the information contained in the listening history, only recently have started to emerge. In this paper we describe a new visualization and exploration tool that allows users to interactively browse their listening histories, while leading them to identify listening trends and habits. Our solution combines a rich-featured timeline-based visualization, a set of synchronized-views and an interactive filtering mechanism to provide a flexible, effective and easy to use system for the analysis and knowledge exploration of listening histories. This was complemented with brushing and highlighting techniques to uncover listening trends about artists, albums and songs. Experimental evaluation with users revealed that they were able to complete all the requested tasks with a low error rate, and that they found the solution flexible and easy to use. Additionally, users were able to infer about their main life events and listening changes, which indicates that our combination of visualization techniques is effective in conveying relevant information about the listening habits.

Uplifting Interviews in Social Science with Individual Data Visualization: the case of Music Listening

“…Each of these plots alone carries less information, but their assembly and cross-interactivity allow for complex visual queries and information retrieval. This approach was followed in [3,5,7] and also by Wirfs-Brock et al [19] in an experiment where a few interviewees were exposed to "silent data" extracted from their Spotify listening history in order to help them defining how they would command a voice assistant to choose music. Although our users are different -namely social scientists conducting interviews -this study is inspirational in using simple plots that are easy to understand for any user, even the less "visually literate".…”

MyEvents: A Personal Visual Analytics Approach for Mining Key Events and Knowledge Discovery in Support of Personal Reminiscence

“…The frequency‐based song‐ranking algorithm is helpful for mood analysis. Dias [DFG12] uses a different timeline‐based layout for the visualization of music‐listening history using stacked dots. Filtering is provided to view only the selected songs and the ranking is purely frequency based.…”

“…This problem cannot be solved by increasing the link thickness due to the notable overlapping caused. Some previous works [BSSB10, DFG12] use a circle to represent events. This can also lead to overlaps if there are many events with long duration.…”

Designing Teenage Emotions with a Life of Their Own

“…The visualization compares two listening histories in a timeline technique using the similarity between two songs and the relevance of the song to the talk. Dias et al developed an approach for browsing listening histories by combining a rich-featured timelinebased visualization with an interactive filtering mechanism, with the goal of helping users to identify trends and habits (6). Their experimental evaluation with users revealed that users were able to infer about their main life events and listening changes, and also promote new behaviors.…”