- Média, Musique, Digital
- Entre 250 et 2000 salariés
Research Internship - Context-Aware Music Recommendation m/f/d
- Éducation : Non spécifié
- Expérience : Non spécifié
Cette offre a été pourvue !
Who are they?
DEEZER IS THE HOME OF MUSIC
From a French tech start-up created in 2007, Deezer has become one of the first French unicorns and the second largest independent music streaming platform in the world.
Now listed at the #Euronext #TechLeaders segment, growth is accelerating fueled by #Thepowerofmusic. Deezer is ideally positioned to play a key role in the continued development of the booming music streaming market. If you’re looking for an environment where you can grow and have an impact, this is the perfect time to join Deezer!
In the music streaming industry, music recommendation is a key component to retain and attract users. Suggesting relevant personalized songs, artists, albums, or playlists helps users actively explore the vast and mostly unknown musical landscape. It is also central to all enjoyable passive experiences relying on generated and personalized content.
To recommend music on Deezer, our team aims to learn the musical preferences of each user, by processing and analyzing their listening history on the service. However, associating users with “fixed” musical tastes would be limiting. Indeed, depending on the context, some users will have different preferences and will listen to music differently. For instance, listening practices can evolve depending on the current activity or the time of the day. In practice, we observe that some of our Deezer users do have very distant preferences depending on the context. For instance, some users prefer to listen to classical music in context A, and to heavy metal in context B. The scientific literature, as well as some of our previous internal investigations, have shown that these contextual aspects can extensively impact the way the same recommendation will be perceived by users.
What you will do:
- study machine learning algorithms that better take into consideration such contextual information, with the aim of recommending the appropriate music to our users.
- in-depth literature review of the existing approaches, as well as an analysis of the most relevant strategies to adopt for music recommendation at Deezer.
- Explore and study a wide range of methods, such as contextual bandits and sequential recommendation algorithms
- Implement the selected methods on actual data extracted from Deezer’s recommender systems.
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, e-learnings and workshops right from the onboarding :
- Regular Diversity & Inclusion internal and external talks
- Dedicated employee work streams on Gender equity, Ethnicity & Culture, Disability and LGBTQ+
- Multiple e-learnings and mandatory training sessions for all managers
- English and French courses for all, so that everyone can connect and feel included
Beyond benefits like transportation, we offer you extra perks like:
- A Deezer premium family account for free
- Access to gym classes
- Deezer parties several times a year and drinks every thursday
- Allowance for sports, travelling and culture …
- Meal vouchers
- Great offices always located in dynamic and attractive districts, whether in Paris, London, Berlin or Sao Paulo!
- Hybrid remote work policy
If you want to learn more about life and culture at Deezer, please visit our Welcome to the Jungle page here!
- Master’s student with a background in Computer Science, Machine Learning, Statistics and/or Applied Mathematics
- Good programming skills and knowledge of machine learning tools (such as scientific Python, Tensorflow, Keras, etc)
- Good data processing and analysis skills
- Creativity and autonomy
- Knowledge and experience in recommender systems would be a plus
- A previous experience in a research environment would be a plus