When the Music Stops: Tip-of-the-Tongue Retrieval for Music


We present a study of Tip-of-the-tongue (ToT) retrieval for music, where a searcher is trying to find an existing music entity, but is unable to succeed as they cannot accurately recall important identifying information. ToT information needs are characterized by complexity, verbosity, uncertainty, and possible false memories. We make four contributions. (1) We collect a dataset of 2,278 information needs and ground truth answers. (2) We introduce a schema for these information needs and show that they often involve multiple modalities encompassing several Music IR sub-tasks such as lyric search, audio-based search, audio fingerprinting, and text search. (3) We underscore the difficulty of this task by benchmarking a standard text retrieval approach on this dataset. (4) We investigate the efficacy of query reformulations generated by a LLM, and show that they are not as effective as simply employing the entire information need as a query–leaving several open questions for future research.


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Structural Podcast Content Modeling with Generalizability

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May 2024 | The Web Conference

Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks

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May 2024 | The Web Conference (GFM workshop)

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