Recommender systems are one of the most sought-after applications of machine learning as they’ve proven to drive significant revenue. For example, Netflix’s recommendation system drives about 80 percent of the content streamed on the platform. While we may not always realize it, personalized recommendations have a significant impact on many aspects of our lives, such as the music we listen to, the content we see and persons we connect with on social media, movies we watch, restaurants we choose…etc.
It is difficult to build a recommender system
It’s no wonder, therefore, that more and more organizations want to integrate a recommendation system within their applications. Building such as system from scratch can, however, represent a daunting task requiring expert data scientists and ML engineers working together to build, tune, and maintain the system over time. All in all, a complex, time-consuming, and costly endeavor requiring expert skills.
How to easily add machine learning-powered recommendations to your applications
Luckily, there’s a much easier and less expensive way. MySQL HeatWave provides in-database machine learning with HeatWave AutoML, which automates the machine learning lifecycle including algorithm selection, intelligent data sampling for model training, feature selection, and hyperparameter optimization. HeatWave AutoML not only supports anomaly detection, forecasting, classification, and regression but also provides a recommender system. By considering both implicit feedback (past purchases, browsing behavior, etc…) and explicit feedback (ratings, likes, etc…), the HeatWave AutoML recommender system generates highly relevant personalized recommendations.
HeatWave AutoML, including its recommender system, is available at no additional cost to MySQL HeatWave customers and you can easily integrate it into your applications. As a matter of fact, you can test it out by building the MovieHub application we’ve developed to showcase the HeatWave AutoML recommender system. You simply need to follow our step-by-step instructions to build this app and won’t even have to write code as you’ll use the most popular low-code development platform, Oracle APEX.
Experience the MySQL HeatWave AutoML recommender system
Start by watching this 2.5 min video to see a demo of the app:
Then build it yourself following the instructions of the workshop:
This will help you get a sense of how you could leverage the MySQL HeatWave AutoML recommender system in various applications such as eCommerce, news platforms, content streaming, online advertising, gaming…etc.
Have fun and let us know if you have any feedback!