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With so much more music available these days, traditional ways of finding music have diminished. Today radio shows are often programmed by large corporations that create playlists drawn from a limited pool of tracks. Similarly, record stores have been replaced by big-box retailers that have ever-shrinking music departments. Instead of relying on DJs, record-store clerks or their friends for music recommendations, listeners are turning to machines to guide them to new music. In this book, Òscar Celma guides us through the world of automatic music recommendation. He describes how music recommenders work, explores some of the limitations seen in current recommenders, offers techniques for evaluating the effectiveness of music recommendations and demonstrates how to build effective recommenders by offering two real-world recommender examples. He emphasizes the user's perceived quality, rather than the system's predictive accuracy when providing recommendations, thus allowing users to discover new music by exploiting the long tail of popularity and promoting novel and relevant material ("non-obvious recommendations"). In order to reach out into the long tail, he needs to weave techniques from complex network analysis and music information retrieval. Aimed at final-year-undergraduate and graduate students working on recommender systems or music information retrieval, this book presents the state of the art of all the different techniques used to recommend items, focusing on the music domain as the underlying application.

Music trade. --- Sound recording industry. --- Music and the Internet --- Music --- User interfaces (Computer systems) --- Recommender systems (Information filtering) --- Engineering & Applied Sciences --- Music, Dance, Drama & Film --- Music Literature --- Computer Science --- Computer network resources --- Music appreciation --- Electronic music. --- Music collection. --- Electronic tape music --- Electronics (Music) --- Electrophonic music --- Music, Electronic --- Tape music --- Tape recorder music --- Analytical guides (Music) --- Appreciation of music --- Musical appreciation --- Analysis, appreciation --- Analytical guides --- Appreciation --- Computer science. --- Music. --- Computer science --- Information storage and retrieval. --- Artificial intelligence. --- Computer Science. --- Information Storage and Retrieval. --- Discrete Mathematics in Computer Science. --- Artificial Intelligence (incl. Robotics). --- Mathematics. --- Computer music --- Musical analysis --- Instruction and study --- Information storage and retrieva. --- Computational complexity. --- Artificial Intelligence. --- Art music --- Art music, Western --- Classical music --- Musical compositions --- Musical works --- Serious music --- Western art music --- Western music (Western countries) --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Complexity, Computational --- Information storage and retrieval systems. --- Automatic data storage --- Automatic information retrieval --- Automation in documentation --- Computer-based information systems --- Data processing systems --- Data storage and retrieval systems --- Discovery systems, Information --- Information discovery systems --- Information processing systems --- Information retrieval systems --- Machine data storage and retrieval --- Mechanized information storage and retrieval systems --- Computer systems --- Electronic information resources --- Data libraries --- Digital libraries --- Information organization --- Information retrieval --- Computer science—Mathematics.