Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners

CHAPTER 15 Case Study of Mobile Application Recommendations

With the rapid growth of smartphones over the past five years, a new market for smartphone applications has emerged and with it stiff competition for mind share. All vendors in this space desire the ability to make recommendations on the applications (apps) that users will like. The goal is to ensure that recommendations are not seen as spam (unwanted solicitations) but instead as great advice, thus moving from simply suggesting content to a highly prized role as a trusted advisor. The business problem in this case is very straightforward: Given the apps users have on their mobile phones, what apps are they likely to use? This problem poses several challenges, the first being the size of the data. With hundreds of millions of cell phone users and each one being almost unique in app purchases, finding good recommendations is difficult. The other issue is the level of detail about the apps. For this business challenge, the data was a set of binary variables. The binary state could be defined a number of different ways: Did they have the application installed? Had they ever used the application? Had the application been used in the last time period?

Regardless of how you frame the problem, the output is a probability to purchase the app (the purchase price may be free) based on a set of binary input variables.

This problem is divided up into several stages, each requiring different skill sets. The first stage is data ...