A few examples of classical online learning algorithms include recursive least squares, stochastic gradient descent and multi-armed bandit algorithms like Thompson sampling. Many online algorithms (including recursive least squares and stochastic gradient descent) have offline versions. These online algorithms are usually developed after the offline version, and are designed for better scaling with large datasets and streaming data. Algorithms like Thompson sampling on the other hand, do not have offline counterparts, because the problems they solve are inherently online.
Let’s look at interactive ad recommendation systems as an example. You’ll find ads powered by these systems when you browse popular publications, weather sites and social media networks. These recommendation systems build customer preference models by tracking your shopping and browsing activities (ad clicking, wish list updates and purchases, for example). Due to the transient nature of shopping behaviors, new recommendations must reflect the most recent activities. This makes online learning a natural choice for these systems.
My favorite online learning algorithm at the moment is Online Passive-Aggressive Algorithms. Not just because that name describes my Twitter feed.
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