A User-Centered Investigation of Personal Music Tours

TL;DR

This study evaluates two algorithms for generating music tours that connect songs with informative segues, revealing user preferences and guiding better music presentation in recommender systems.

Contribution

First user-centered evaluation of tour-generation algorithms, analyzing their attributes, preferences, and potential improvements for music recommendation systems.

Findings

Greedy algorithm produces more likeable tours than Optimal

Segue diversity, song arrangement, and familiarity are key tour attributes

Insights can inform user-centered music recommender system design

Abstract

Streaming services use recommender systems to surface the right music to users. Playlists are a popular way to present music in a list-like fashion, ie as a plain list of songs. An alternative are tours, where the songs alternate segues, which explain the connections between consecutive songs. Tours address the user need of seeking background information about songs, and are found to be superior to playlists, given the right user context. In this work, we provide, for the first time, a user-centered evaluation of two tour-generation algorithms (Greedy and Optimal) using semi-structured interviews. We assess the algorithms, we discuss attributes of the tours that the algorithms produce, we identify which attributes are desirable and which are not, and we enumerate several possible improvements to the algorithms, along with practical suggestions on how to implement the improvements. Our main findings are that Greedy generates more likeable tours than Optimal, and that three important attributes of tours are segue diversity, song arrangement and song familiarity. More generally, we provide insights into how to present music to users, which could inform the design of user-centered recommender systems.