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You will be updated with latest job alerts via emailCurrent recommendation systems rely heavily on latent collaborative filtering (CF) models which analyze usage data to generate lowdimensional embeddings for users and tracks. However while effective CFbased models have inherent limitations such as limited coverage of the catalog and slow integration of new releases in the recommendation engine. This internship will address these challenges by exploring multimodal machine learning approaches. By integrating diverse data sources such as CF text and audio embeddings these methods aim to create a comprehensive multimodal embedding space for tracks leveraged for downstream recommendation / retrieval tasks.
Conduct an indepth review of stateoftheart multimodal methods.
Design and implement multimodal models for recommendation and/or retrieval.
Apply these models to realworld Deezer datasets and benchmark their performance.
Optionally contribute to publications and / or conduct A/B testing of selected methods in a production environment.
Qualifications :
Master / PhD student with a background in Computer Science / Applied Mathematics / Statistics.
Strong knowledge of music analysis natural language processing applied machine learning and data mining
Good programming skills for data processing and experimentation (preferred python)
Creativity and autonomy
Additional Information :
At Deezer you can be your true self as we believe that #everyvoicematters. We strive to build an inclusive culture and foster a diverse environment. Because we care and want to ensure each employee feels welcome and safe at work we continuously focus on fighting biases and helping diverse teams work well together through multiple learning opportunities elearnings and workshops right from the onboarding :
Beyond benefits like transportation we offer you extra perks like:
If you want to learn more about life and culture at Deezer please visit our Welcome to the Jungle page here!
Remote Work :
No
Employment Type :
Intern
Intern