We wish to2 learn a hiddend-dimensional valuew . Every round, we are presented with a3 subset Xt Rd of possible actions.

We wish to learn a hiddend-dimensional valuew . Every round, we are presented with a subset Xt Rd of possible actions.

We consider the following variant of contextual linear bandits motivated by routing applications in navigational engines and recommendation systems. We wish to learn a hidden $d$-dimensional value $w^*$. Every round, we are presented with a subset $\mathcal{X}_t \subseteq \mathbb{R}^d$ of possible actions.

Consider the following problem faced by a geographical query service (e.g.

We consider the following variant of contextual linear bandits motivated by routing applications in navigational engines and recommendation systems. We wish to learn a hidden d -dimensional value w * . Every round, we are presented with a subset \mathcal{X}_t \subseteq \mathbb{R} d of possible actions. To accomplish this, we design novel cutting-plane algorithms with low "regret" -- the total distance between the true point w * and the hyperplanes the separation oracle returns. We also consider the variant where we are allowed to provide a list of several recommendations.

I recently gave a talk about recommender systems at the Data Science Sydney meetup (the slides are available here). This post roughly follows the outline of the talk, expanding on some of the key points in non-slide form (i.e., complete sentences and paragraphs!). The first few sections give a broad overview of the field and the common recommendation paradigms, while the final part is dedicated to debunking five common myths about recommender systems. The key reason why many people seem to care about recommender systems is money. For companies such as Amazon, Netflix, and Spotify, recommender systems drive significant engagement and revenue. But this is the more cynical view of things.