Apple Music upgrades search with multilingual AI model

Apple Music's search challenge is rooted in the sheer scale and diversity of its service: 150+ storefronts, dozens of languages, and a catalog that grows by hundreds of thousands of tracks daily. In this environment, traditional keyword matching fails on a large class of legitimate user queries—misspellings, transliterations (such as Latin spellings of non-Latin scripts), and searches that cross language boundaries. The company identifies tail queries—rare or one-off searches that collectively represent the majority of unique searches—as the decisive factor in session quality. A user searching for an artist in a language different from their interface, or misremembering a song title's spelling, will bounce if the system fails to find the track. By building a multilingual semantic retrieval system rather than relying on exact-match or rule-based approaches, Apple aims to understand the intent behind these difficult queries and surface the correct track. The use of a Siamese bi-encoder architecture (which compares query and track embeddings) and curriculum-scheduled multi-objective training suggests the model was trained to balance multiple goals—exact matching, semantic similarity, language robustness—in a staged way that improves generalization.