GetJar mobile application recommendations with very sparse datasets

Abstract

The Netflix competition of 2006 [2] has spurred significant activity in the recommendations field, particularly in approaches using latent factor models [3,5,8,12] However, the near ubiquity of the Netflix and the similar MovieLens datasets1 may be narrowing the generality of lessons learned in this field. At GetJar, our goal is to make appealing recommendations of mobile applications (apps). For app usage, we observe a distribution that has higher kurtosis (heavier head and longer tail) than that for the aforementioned movie datasets. This happens primarily because of the large disparity in resources available to app developers and the low cost of app publication relative to movies.