Striking the right chord: A comprehensive approach to Amazon Music search spell correction

Music and media search spell correction is distinct as it involves named entities like artist, album and podcast names, keywords from track titles and catchy phrases from lyrics. Users often mix artist names and keywords from track title or lyrics making spell correction highly contextual. Data drift in search queries caused during calendar event days or a newly released music album, brings a unique challenge of quickly adapting to new data points. Scalability of the solution is an essential requirement as the Music catalog is extremely large. In this work, we build a multi-stage framework for spell correction solution for music, media and named entity heavy search engines. We offer contextual spelling suggestions using a generative text transformer model and a mechanism to rapidly adapt to data drift as well as different market needs by using parameter efficient based fine tuning techniques. Further-more, using a reinforcement learning approach our spell correction system can learn from a user’s implicit and explicit feedback in real-time. Some key components of this system are being used in search at Amazon Music and showing significant improvements in customer engagement rate and other relevant metrics.