2012 GetJarMobileApplicationRecommen

• (Shi & Ali, 2012) ⇒ Kent Shi, and Kamal Ali. (2012). “GetJar Mobile Application Recommendations with Very Sparse Datasets.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339563

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Cited By

• http://scholar.google.com/scholar?q=%222012%22+GetJar+Mobile+Application+Recommendations+with+Very+Sparse+Datasets
• http://dl.acm.org/citation.cfm?id=2339530.2339563&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; evaluation; information filtering; mobile application; pca; recommender system; sparse data

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.

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 GetJarMobileApplicationRecommen	Kent Shi Kamal Ali			GetJar Mobile Application Recommendations with Very Sparse Datasets				10.1145/2339530.2339563		2012