A theory of information matching
Abstract
* The theory and the mathematical modelling presented in this report has not been published elsewhere. However, different applications of the theory are under review.
References (28)
- BLEI, D. M., NG, A. Y., AND JORDAN, M. I. Latent dirichlet allocation. J. Mach. Learn. Res. 3 (Mar. 2003).
- BRIN, S., AND PAGE, L. The anatomy of a large-scale hypertextual web search engine. In WWW (1998).
- BURGES, C. J. C., RAGNO, R., AND LE, Q. V. Learning to rank with nonsmooth cost functions. In NIPS (2006), pp. 193-200.
- CRASWELL, N., ROBERTSON, S., ZARAGOZA, H., AND TAYLOR, M. Relevance weight- ing for query independent evidence. In SIGIR (2005), SIGIR '05.
- DEMPSTER, A., LAIRD, N., AND RUBIN, D. Maximum likelihood from incomplete data via the em algorithm. J. Royal Statistical Society, Series B (1977).
- DIEBOLT, J., AND ROBERT, C. P. Estimation of finite mixture distributions through bayesian sampling. J. R. Statist. Soc B, 2 (1994), 363-375.
- GAO, J., YUAN, W., LI, X., DENG, K., AND NIE, J.-Y. Smoothing clickthrough data for web search ranking. In SIGIR (2009), pp. 355-362.
- HARTER, S. A probabilistic approach to automatic keyword indexing. Journal of the Amer- ican Society for Information Retrieval Science (1975).
- HOFMANN, T. Probabilistic latent semantic indexing. In SIGIR (1999), SIGIR '99.
- HOFMANN, T. Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. (2004).
- HUANG, X., AND CROFT, W. B. A unified relevance model for opinion retrieval. In In CIKM (2009), CIKM '09.
- JONES, K. S. A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation 28 (1972), 11-21.
- KOREN, Y., BELL, R. M., AND VOLINSKY, C. Matrix factorization techniques for recom- mender systems. IEEE Computer (2009).
- LAFFERTY, J. D., AND ZHAI, C. Document language models, query models, and risk minimization for information retrieval. In SIGIR (2001), pp. 111-119.
- MARON, M. E., AND KUHNS, J. L. On relevance, probabilistic indexing and information retrieval. J. ACM (1960).
- MEI, Q., FANG, H., AND ZHAI, C. A study of poisson query generation model for infor- mation retrieval. In In SIGIR (2007), SIGIR '07.
- MIZARRO, S. Relevance: The whole history. Journal of the American Society for Informa- tion Science 48(9) (1997), 321-343.
- PONTE, J. M., AND CROFT, W. B. A language modeling approach to information retrieval. In SIGIR (1998).
- ROBERTSON, S. The unified model revisited. In SIGIR 2003 Workshop on Mathemati- cal/Formal Models in Information Retrieval (2003).
- ROBERTSON, S., MARON, M. E., AND COOPER, W. S. Probability of relevance: a unifi- cation of two competing models for document retrieval. Information Technology: Research and Development (1982).
- ROBERTSON, S., AND SPARK JONES, K. Relevance weighting of search terms. Journal of the American Society for Information Science (1976).
- ROBERTSON, S., AND ZARAGOZA, H. The probabilistic relevance framework: BM25 and beyond. Found. Trends Inf. Retr. (2009).
- ROBERTSON, S. E., VAN RIJSBERGEN, C. J., AND PORTER, M. F. Probabilistic models of indexing and searching. In SIGIR (1980).
- ROBERTSON, S. E., AND WALKER, S. Some simple effective approximations to the 2- poisson model for probabilistic weighted retrieval. In SIGIR (1994).
- SARWAR, B., KARYPIS, G., KONSTAN, J., AND REIDL, J. Item-based collaborative filter- ing recommendation algorithms. In WWW (2001).
- SCHOLER, F., AND WILLIAMS, H. E. Query association for effective retrieval. In CIKM (2002), CIKM '02.
- SHARDANAND, U., AND MAES, P. Social information filtering: algorithms for automating ẅord of mouth ¨. In CHI'95 (1995).
- WANG, J., DE VRIES, A. P., AND REINDERS, M. J. Unified relevance models for rating prediction in collaborative filtering. ACM Trans. on Information System (TOIS) (2008).