Recommending Music with Waveform-Based Architectures
Recommending Music with Waveform-based Architectures ORIOL NIETO GLOBAL BIG DATA CONFERENCE SANTA CLARA, CA JAN 21, 2019 @urinieto Pandora Confidential OUTLINE Background: Collaborative Filtering Music Recommendation Demo OUTLINE Background: Collaborative Filtering Music Recommendation Demo Collaborative Filtering RECOMMENDING “POPULAR” ITEMS ? ? [ ? ? ? ? Items (Tracks) ? ? [ ? ? Users Collaborative Filtering PROBLEM OVERVIEW [ [ k ? ? [ ? ? ? ? k Items (Tracks) ? ? ⇡ Items (Tracks) ? ? [ [ [ Users Users Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix Factorization Techniques for Recommender Systems. Computer, 42(8), 42–49. Collaborative Filtering PROBLEM OVERVIEW [ [ k ? ? [ ? ? ? ? k Items (Tracks) ? ? ⇡ Items (Tracks) ? ? [ [ [ Users Users Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix Factorization Techniques for Recommender Systems. Computer, 42(8), 42–49. Collaborative Filtering PROBLEM OVERVIEW [ [ k ? ? [ ? ? ? ? k Items (Tracks) ? ? ⇡ Items (Tracks) ? ? [ [ [ Users Users Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix Factorization Techniques for Recommender Systems. Computer, 42(8), 42–49. Collaborative Filtering LATENT FACTORS Complex Harmony Calm Aggressive Simple Harmony Collaborative Filtering THE GOOD AND THE BAD Rich preference-driven similarity space Latent space is generally not interpretable Can only recommend items tHat Powerful at MatcHing tHe rigHt song have already been rated witH tHe right listener (what about long tail content?) OUTLINE Background: Collaborative Filtering Music RecoMMendation Demo Music
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