NDLI: GetJar mobile application recommendations with very sparse datasets

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GetJar mobile application recommendations with very sparse datasets

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GetJar mobile application recommendations with very sparse datasets

Content Provider	ACM Digital Library
Author	Ali, Kamal Shi, Kent
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 $datasets^{1}$ 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. In this paper we compare a latent factor (PureSVD) and a memory-based model with our novel PCA-based model, which we call Eigenapp. We use both accuracy and variety as evaluation metrics. PureSVD did not perform well due to its reliance on explicit feedback such as ratings, which we do not have. Memory-based approaches that perform vector operations in the original high dimensional space over-predict popular apps because they fail to capture the neighborhood of less popular apps. They have high accuracy due to the concentration of mass in the head, but did poorly in terms of variety of apps exposed. Eigenapp, which exploits neighborhood information in low dimensional spaces, did well both on precision and variety, underscoring the importance of dimensionality reduction to form quality neighborhoods in high kurtosis distributions.
Starting Page	204
Ending Page	212
Page Count	9
File Format	PDF MP4
ISBN	9781450314626
DOI	10.1145/2339530.2339563
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-08-12
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Evaluation Sparse data Mobile application Recommender system Pca
Content Type	Audio Text
Resource Type	Article

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