The search engine of music streaming platforms is a high-control method for navigating the catalog. If one is to study users’ search behavior in this context, one can leverage the vast body of research on general information behavior while challenging previously well validated models with the domain-specific differences. Due to the nature of musical content, users present a series of different needs and behaviors than on traditional web search. For instance, some users employ the search engine as a means to drive their listening session, inputting many queries in close succession not related to the same information goal.

In this paper, we investigate users’ search goals and how they modulate information behavior in the context of streaming platforms. To this end, we explore real search sessions of users looking for musical content in the context of a major streaming service. We introduce a data-driven method for identifying classes of information needs by aggregating both low-level activity patterns and relative query specificity. We show that, when combined, these features provide an approach not only for isolating classes of user search intent, but for understanding human-music relationship as a whole.

This paper has been accepted for publication in the proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (UMAP 2022).