Robust multivariate autoregression for anomaly detection in dynamic product ratings

Nikou Günnemann, Stephan Günnemann, Christos Faloutsos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

44 Scopus citations

Abstract

User provided rating data about products and services is one key feature of websites such as Amazon, TripAdvisor, or Yelp. Since these ratings are rather static but might change over time, a temporal analysis of rating distributions provides deeper insights into the evolution of a products' quality. Given a time-series of rating distributions, in this work, we answer the following questions: (1) How to detect the base behavior of users regarding a product's evaluation over time? (2) How to detect points in time where the rating distribution differs from this base behavior, e.g., due to attacks or spontaneous changes in the product's quality? To achieve these goals, we model the base behavior of users regarding a product as a latent multivariate autoregressive process. This latent behavior is mixed with a sparse anomaly signal finally leading to the observed data. We propose an efficient algorithm solving our objective and we present interesting findings on various real world datasets. Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Original languageEnglish
Title of host publicationWWW 2014 - Proceedings of the 23rd International Conference on World Wide Web
PublisherAssociation for Computing Machinery
Pages361-371
Number of pages11
ISBN (Electronic)9781450327442
DOIs
StatePublished - 7 Apr 2014
Externally publishedYes
Event23rd International Conference on World Wide Web, WWW 2014 - Seoul, Korea, Republic of
Duration: 7 Apr 201411 Apr 2014

Publication series

NameWWW 2014 - Proceedings of the 23rd International Conference on World Wide Web

Conference

Conference23rd International Conference on World Wide Web, WWW 2014
Country/TerritoryKorea, Republic of
CitySeoul
Period7/04/1411/04/14

Keywords

  • Anomaly detection
  • Robust autoregression
  • Sparsity

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