ABSTRACT
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.
Supplemental Material
- R. Agrawal, S. Ieong, and R. Velu. Ameliorating buyers' remorse. In Proc. KDD, 2011. Google ScholarDigital Library
- R. Agrawal, S. Ieong, and R. Velu. Timing when to buy. In Proc. CIKM, 2011. Google ScholarDigital Library
- O. Benjelloun, H. Garcia-Molina, D. Menestrina, Q. Su, S. E. Whang, and J. Widom. Swoosh: a generic approach to entity resolution. The VLDB Journal, 18(1):255--276, 2009. Google ScholarDigital Library
- M. Bilenko, R. Mooney, W. Cohen, P. Ravikumar, and S. Fienberg. Adaptive name matching in information integration. IEEE Intel. Sys., 18(5):16--23, 2003. Google ScholarDigital Library
- C. M. Bishop. Pattern Recognition and Machine Learning. Springer-Verlag New York, 2006. Google ScholarDigital Library
- A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal Of The Royal Statistical Society, Series B, 39(1):1--38, 1977.Google Scholar
- A. K. Elmagarmid, P. G. Ipeirotis, and V. S. Verykios. Duplicate record detection: A survey. IEEE Trans. on Knowl. and Data Eng., 19(1):1--16, 2007. Google ScholarDigital Library
- I. P. Fellegi and A. B. Sunter. A theory for record linkage. Journal of the American Statistical Association, 64(328):1183--1210, 1969.Google ScholarCross Ref
- Z. Gharamani and G. E. Hinton. Parameter estimation for linear dynamical systems. Technical report, University of Toronto, 1996.Google Scholar
- Z. Gharamani and G. E. Hinton. Variational learning for switching state-space models. Neural Computation, 12(4):963--996, 2000. Google ScholarDigital Library
- N. J. Gordon, D. J. Salmond, and A. F. M. Smith. Novel approach to nonlinear/non-gaussian bayesian state estimation. Radar and Signal Processing, IEE Proceedings F, 140(2):107--113, 1993.Google Scholar
- P. J. Huber. Robust Statistics. Wiley, 1981.Google Scholar
- M. Isard and A. Blake. CONDENSATION - conditional density propagation for visual tracking. Int. Journal of Computer Vision, 29(1):5--18, 1998. Google ScholarDigital Library
- R. E. Kalman. A new approach to linear filtering and prediction problems. Transactions of the ASME-Journal of Basic Engineering, 82(Series D):35--45, 1960.Google Scholar
- K. Kanazawa, D. Koller, and S. Russel. Stochastic simulation algorithms for dynamic probabilistic networks. In Proc. UAI, 1995. Google ScholarDigital Library
- A. Kannan, I. E. Givoni, R. Agrawal, and A. Fuxman. Matching unstructured product offers to structured product specifications. In Proc. KDD, 2011. Google ScholarDigital Library
- M. Michelson and C. Knoblock. Creating relational data from unstructured and ungrammatical data sources. Journal of Artificial Intelligence Research, 31:543--590, 2008. Google ScholarDigital Library
- R. Mitkov. Anaphora Resolution. Longman, 2002.Google Scholar
- H. B. Newcombe, M. J. Kennedy, S. J. Axford, and A. P. James. Automatic linkage of vital records. Science, 130:954--959, October 1959.Google ScholarCross Ref
- P. Ravikumar and W. W. Cohen. A hierarchical graphical model for record linkage. In UAI, 2004. Google ScholarDigital Library
- S. Roweis and Z. Ghahramani. A unifying review of linear gaussian models. Neural Computation, 1997. Google ScholarDigital Library
- S. Sarawagi and A. Bhamidipaty. Interactive deduplication using active learning. In KDD, pages 269--278, 2002. Google ScholarDigital Library
- S. Singh, K. Schultz, and A. McCallum. Bi-directional joint inference for entity resolution and segmentation using imperatively-defined factor graphs. In ECML-PKDD, pages 414--429, 2009. Google ScholarDigital Library
- B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. I. Jordan, and S. Sastry. Kalman filtering with intermittent observations. IEEE Trans. on Automatic Control, 49:1453--1464, 2004.Google ScholarCross Ref
- W. E. Winkler. Overview of record linkage and current research directions. Technical report, Bureau of the Census, 2006.Google Scholar
- P. Zarchan and H. Musoff. Fundamentals of Kalman Filtering: A Practical Approach. AIAA, 2nd ed., 2005.Google Scholar
- J. Zhong and S. Sclaroff. Segmenting foreground objects from a dynamic textured background via a robust kalman filter. In Proc. ICCV, 2003. Google ScholarDigital Library
Index Terms
- Aggregating web offers to determine product prices
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