Real-time Machine Learning For Recommendations

Besides the three dimensions, scale, reliability, and the speed of iteration, there are other dimensions that have a major impact on the shape of the solution. Consider the following:

Speed (time to response)

Slow : Results needed in minutes

e.g. portfolio optimization

Fast : Results needed in milliseconds

e.g. high-frequency trading

Scale (requests/sec... See more

in recommendation media, the algorithms that power distribution reign supreme. These algorithms, which are powered by machine learning, are unique, valuable, and grow in power and accuracy as a platform scales. Therefore, only the biggest and most powerful platforms can afford investments in the best machine learning algorithms because they are suc... See more

A recommender system is a digital product that uses AI to generate personalized recommendations. Over the past 20 years, recommender systems have come to play a central role on many large online platforms. Some recommender systems run prominently in the foreground of the user experience (UX), and operate as core features or even brands (such as Spo... See more

These gray-area business logic questions where recommender systems become just as much of an art as a science, continuing to generate potential content for recommendation, blending it, filtering out negative or prohibited content on the platform to get to a “good” feed of engageable content.

All of the effort spent deliberating on edge cases and long tails stems from the fact that many junior devs are not actually thinking hard enough about what the experiment should be, and what the metrics should look like.

The goal of building out these probabilistic software systems is not a milestone or a feature. Instead, what we're looking for a... See more