Search Mindsets: Understanding Focused and Non-Focused Information Needs in Music Search

Abstract

Music listening is a commonplace activity that has transformed as users engage with online streaming platforms. When presented with anytime, anywhere access to a vast catalog of music, users face challenges in searching for what they want to hear. We propose that users who engage in domain-specific search (e.g., music search) have different information-seeking needs than in general search. Using a mixed-method approach that combines a large-scale user survey with behavior data analyses, we describe the construct of search mindset on a leading online streaming music platform and then investigate two types of search mindsets: focused, where a user is looking for one thing in particular, and non-focused, where a user is open to different results. Our results reveal that searches in the music domain are more likely to be focused than non-focused. In addition, users’ behavior (e.g., clicks, streams, querying, etc.) on a music search system is influenced by their search mindset. Finally, we propose design implications for music search systems to best support their users.

Related

June 2024 | ICWSM

Socially-Motivated Music Recommendation

Ben Lacker, Samuel Way

May 2024 | Yijun Tian, Maryam Aziz, Alice Wang, Enrico Palumbo and Hugues Bouchard

Structural Podcast Content Modeling with Generalizability

Yijun Tian, Maryam Aziz, Alice Wang, Enrico Palumbo and Hugues Bouchard

May 2024 | The Web Conference

Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks

Marco De Nadai, Francesco Fabbri, Paul Gigioli, Alice Wang, Ang Li, Fabrizio Silvestri, Laura Kim, Shawn Lin, Vladan Radosavljevic, Sandeep Ghael, David Nyhan, Hugues Bouchard, Mounia Lalmas-Roelleke, Andreas Damianou