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User group analytics: hypothesis generation and exploratory analysis of user data

Authors
  • Omidvar-Tehrani, Behrooz
  • Amer-Yahia, Sihem
  • Borromeo, Ria Mae
Type
Published Article
Journal
The VLDB Journal
Publisher
Springer Berlin Heidelberg
Publication Date
Oct 26, 2018
Volume
28
Issue
2
Pages
243–266
Identifiers
DOI: 10.1007/s00778-018-0527-4
Source
Springer Nature
Keywords
License
Yellow

Abstract

User data is becoming increasingly available in multiple domains ranging from the social Web to retail store receipts. User data is described by user demographics (e.g., age, gender, occupation) and user actions (e.g., rating a movie, publishing a paper, following a medical treatment). The analysis of user data is appealing to scientists who work on population studies, online marketing, recommendations, and large-scale data analytics. User data analytics usually relies on identifying group-level behavior such as “Asian women who publish regularly in databases.” Group analytics addresses peculiarities of user data such as noise and sparsity to enable insights. In this paper, we introduce a framework for user group analytics by developing several components which cover the life cycle of user groups. We provide two different analytical environments to support “hypothesis generation” and “exploratory analysis” on user groups. Experiments on datasets with different characteristics show the usability and efficiency of our group analytics framework.

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