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Domains / Music, Podcasts

Keep Listeners Coming Back With Personalized Recommendations

Recommend the next song, artist, album, or playlist based on listener context and real-time behavior to drive discovery, engagement, and repeat listening.

Music, Podcasts
  • Audiomack
  • Sleepiest
  • Voxa
  • Airbit
  • Singa

Use Cases

  • Fully Personalized Homepage

    Automate and tailor all your homepage rows 1:1 for each user.

  • Similar Songs/Artists

    Present new songs and podcasts based on the content the user is currently enjoying.

  • Playlists Made For You

    Customize playlists by showing the most relevant songs or podcasts from a pre-selected list.

  • New Releases

    Give listeners an overview of the week's new releases.

  • Recommended Artists For You

    Inspire your users with work from artists they might enjoy based on their listening history.

  • Quick Search

    Match search queries and tailor the music and podcast selection to individual preferences.


Recommend Songs, Albums, Artists and Podcasts

Recombee automatically understands the links between songs, albums, and artists.

Uplift Revenues, Boost Playtime, Subscriptions and Retention Rates

Increase number of plays, active users, artist follows, ad revenues and other metrics.

Offer Great Personalized Recommendations for Music and Podcasts

Recommend music that fits the user using advanced collaborative filtering models.

Recommend Local and Niche Content

Allow listeners to discover unique content creators within a large catalog of songs and podcasts, including sensitivity to particular cultural regions.

Provide Real-Time Responses To Mood Changes

Recommend content selections based on different situations and time of the day.

Control the Behaviour of Recommendations

Use Recombee Business rules to set constraints, boosters, filter out content with explicit lyrics or by unverified artists.


Recombee Insights

Real-Time Analytics & Insights

Take a deep dive into your data using our advanced real-time analytics tool called Insights within the Recombee Admin UI.

  • Analyze top-recommended content, trending artists or albums, and other aspects of user behavior such as local content consumption
  • Visualize your data, create custom reports, and seamlessly share insights with your team
  • Choose from a library of analytical views or create your graphs and reports
  • Using the analytical data, adjust the rules and let the recommender engine follow your product vision
Explore Insights

"Striving to be the ever limitless music sharing and discovery platform, we need to make sure the user experience of our listeners is smooth and sound. And one of the most critical aspects of achieving such a goal is content personalization tailored 1:1 in real-time. That's why we switched to Recombee. Thanks to their recommender engine, our monthly plays increased by 206% and weekly follows by 67%. Because the recommendations performed so well, we moved them from our Search page to the top of our main Discover tab. They are now the best-performing module within that tab, accounting for 46% of all plays."
Christopher Dalla Riva
Christopher Dalla Riva
Senior Product Manager at Audiomack

Increase the Number of Subscribers and Time Spent With Personalized Playlists and Podcasts

With more aspiring artists and easy access to music and podcasts, it is no easy task to stay competitive. We analyze consumed content, favorite artists, speakers, genres, or descriptions in multiple languages, to help your platform offer podcasts and music recommendations tailored to personal tastes.

Beyond basic data, Recombee’s recommendation engine works with information about which songs/podcasts were listened to till the end, which halfway or skipped completely. Utilize Recombee to offer recommendations of genres, artists, songs or playlists to keep the listener entertained and eager to revisit your platform.

Recombee’s robust recommendation engine analyzes item properties such as title, genre, author, language or tags and interactions like view, replay, like or rating. Autoplay is one of many features that Recombee offers to the listeners and enhances their time spent on the platform.

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Case Studies


Core Technology

  • Adapting to Your Data

    A robust system that can utilize all data available to generate great recommendations for your users, including collaborative filtering and content-based models.

  • Dynamically Retrained Models

    Real-time content personalization that adapts to the flourishing customer’s tastes and considers the newly added music or podcast content.

  • Specific Functionalities for Music Platforms

    Recommendations taking into account the users’ listening time; which titles were listened to until the end, which were listened to halfway or skipped completely.

  • AI-powered A/B Testing

    In-house AutoML AI that maximizes KPIs and continuously optimizes deep learning algorithms.

  • Advanced Business Rules

    Boosters or filters to push forward desired songs or genres and easy to manipulate, adjustable rules for additional optimization of your content.

  • Real AI Inside

    Reinforcement learning and other algorithms designed to recognize the preferences of individual users and predict desired music or podcast with higher accuracy boosting user engagement.

"Working with Recombee to develop an affordable solution to provide our users with excellent music recommendations has exceeded our expectations in every way. They have been able to understand the relationships in the data of our industry and create effective models to use efficiently. We also appreciate the nuance and flexibility they offer when it comes to deciding the right solution based on quality, cost, complexity, speed, and other factors."
Ty Wangsness
Ty Wangsness
Founder/CTO at Audiomack

Chosen Customers

  • Audiomack
  • Stingray
  • Airbit
Explore Success Stories

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