Linear Digressions

The Cold Start Problem

Linear Digressions

You might sometimes find that it's hard to get started doing something, but once you're going, it gets easier. Turns out machine learning algorithms, and especially recommendation engines, feel the same way. The more they "know" about a user, like what movies they watch and how they rate them, the better they do at suggesting new movies, which is great until you realize that you have to start somewhere. The "cold start" problem will be our focus in this episode, both the heuristic solutions that help deal with it and a bit of realism about the importance of skepticism when someone claims a great solution to cold starts. Relevant links: http://repository.upenn.edu/cgi/viewcontent.cgi?article=1141&context=cis_papers

Next Episodes

Linear Digressions

Open Source Software for Data Science @ Linear Digressions

📆 2016-09-19 06:27 / 00:20:05


Linear Digressions

Scikit + Optimization = Scikit-Optimize @ Linear Digressions

📆 2016-09-12 03:54 / 00:15:41


Linear Digressions

Two Cultures: Machine Learning and Statistics @ Linear Digressions

📆 2016-09-05 03:50 / 00:17:29


Linear Digressions

Optimization Solutions @ Linear Digressions

📆 2016-08-29 04:01 / 00:20:07


Linear Digressions

Optimization Problems @ Linear Digressions

📆 2016-08-22 02:25 / 00:17:50