CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
A unifying review of linear Gaussian models
Neural Computation
Interactive deduplication using active learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Segmenting Foreground Objects from a Dynamic Textured Background via a Robust Kalman Filter
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A hierarchical graphical model for record linkage
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Variational Learning for Switching State-Space Models
Neural Computation
Adaptive Name Matching in Information Integration
IEEE Intelligent Systems
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Duplicate Record Detection: A Survey
IEEE Transactions on Knowledge and Data Engineering
Swoosh: a generic approach to entity resolution
The VLDB Journal — The International Journal on Very Large Data Bases
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Creating relational data from unstructured and ungrammatical data sources
Journal of Artificial Intelligence Research
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Matching unstructured product offers to structured product specifications
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 20th ACM international conference on Information and knowledge management
Stochastic simulation algorithms for dynamic probabilistic networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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Historical prices are important information that can help consumers decide whether the time is right to buy a product. They provide both a context to the users, and facilitate the use of prediction algorithms for forecasting future prices. To produce a representative price history, one needs to consider all offers for the product. However, matching offers to a product is a challenging problem, and mismatches could lead to glaring errors in price history. We propose a principled approach to filter out erroneous matches based on a probabilistic model of prices. We give an efficient algorithm for performing inference that takes advantage of the structure of the problem. We evaluate our results empirically using merchant offers collected from a search engine, and measure the proximity of the price history generated by our approach to the true price history. Our method outperforms alternatives based on robust statistics both in tracking the true price levels and the true price trends.