Any discussion in the world of data science and machine learning is incomplete without the mention of prediction and recommendation engines. Recommendation engines have become the shining star of big data. Building a recommendation engine is at the heart of modern marketing with user level personalization becoming the secret to success for media and retail business domains. It is extremely important for data scientists to follow the right approach when building a recommendation engine as it is a big investment for any organization both technically and financially.
With unlimited music streaming services, it has become important to personalize algorithms, however this would be possible only if there is enough historical data. Currently KKBOX uses a collaborative filtering music recommendation engine but aims to employ novel data science techniques which could lead to better results. The new recommendation algorithm would know if a listener likes a new artist or a new song and what kind of song to recommend to brand new users.
Working on data science projects is an important milestone in the journey towards becoming an enterprise data scientist. This data science project aims to help data scientists/data analysts learn how to build a recommendation engine with the end goal of reducing churn, enhancing user experience, and increasing profitability for business success.
You will work with Asia’s leading music streaming brand KKBOX dataset that provides over 40 million tracks at your fingertips having over 10 million members across the region. The training dataset contains information on the first observable listening event for every user-song pair in a given time duration. The dataset also provides metadata information for every user and song pair.
So, what are you waiting for?
Enrol now to learn how to build the best music recommendation engine using KKBOX Dataset for just $9.