Analysis of User Needs and Information Features in Natural Language Queries Seeking Music Information

Our limited understanding of real-life queries is an obstacle in developing music information retrieval (MIR) systems that meet the needs of real users. This study aimed, by an empirical investigation of real-life queries, to contribute to developing a theorized understanding of how users seek music information. This is crucial for informing the design of future MIR systems, especially the selection of potential access points, as well as establishing a set of test queries that reflect real-life music information-seeking behavior. Natural language music queries were collected from an online reference Website and coded using content analysis. A taxonomy of user needs expressed and information features used in queries were established by an iterative coding process. This study found that most of the queries analyzed were known-item searches, and most contained a wide variety of kinds of information, although a few features were used much more heavily than the others. In addition to advancing our understanding of real-life user queries by establishing an improved taxonomy of needs and features, three recommendations were made for improving the evaluation of MIR systems: (i) incorporating user context in test queries, (ii) employing terms familiar to users in evaluation tasks, and (iii) combining multiple task results.