Analysis of user needs and information features in natural language queries seeking music information

Abstract

AbstractOur 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.