In this project you will be concerned with optimizing the www.netflix.com homepage by way of minimizing browsing time.
Browsing time is the length of time a user spends browsing (as opposed to watching) Netflix. Ideally, browsing time and, in particular, average browsing time would be small. In this project I have conducted a series of experiments to learn what influences browsing time and how that may be exploited in order to minimize average browsing time. There are infinitely many things that likely influence the amount of time someone spends browsing Netflix, but just four factors will be explored in this project which are: Tile Size, Match Score, Preview Length and Preview Type.
Through a series of experiments I will seek to determine which of these factors significantly influences browsing time, and I also attempt to find an optimal configuration of them that minimizes expected browsing time. I do this by interacting with a web-based simulator (https://nathaniel-t-stevens.shinyapps.io/Netflix_Simulator_v4/), which I will use to receive response observations.
References:
- Thanks to Dr. Nathaniel Stevens' 'MSDS629: Experiments in Data Science' course at the University of San Francisco!
- Thanks to Shagun Kala, Obtin Zandian and Mark Lam for being brilliant teammates!