Sparse Approximate Solutions to Linear Systems
SIAM Journal on Computing
Independent component analysis: algorithms and applications
Neural Networks
Complexity of Quantifier Elimination in the Theory of Algebraically Closed Fields
Proceedings of the Mathematical Foundations of Computer Science 1984
Learning an Outlier-Robust Kalman Filter
ECML '07 Proceedings of the 18th European conference on Machine Learning
Dynamics of a Collaborative Rating System
Advances in Web Mining and Web Usage Analysis
Multi-facet Rating of Product Reviews
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
New Introduction to Multiple Time Series Analysis
New Introduction to Multiple Time Series Analysis
Detection of the customer time-variant pattern for improving recommender systems
Expert Systems with Applications: An International Journal
Proceedings of the 20th international conference on World wide web
A generalized stochastic block model for recommendation in social rating networks
Proceedings of the fifth ACM conference on Recommender systems
Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy
Proceedings of the fifth ACM conference on Recommender systems
Direct Robust Matrix Factorizatoin for Anomaly Detection
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Outlier Analysis
Change Detection in Streaming Multivariate Data Using Likelihood Detectors
IEEE Transactions on Knowledge and Data Engineering
Incorporating author preference in sentiment rating prediction of reviews
Proceedings of the 22nd international conference on World Wide Web companion
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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.