ABSTRACT
Evaluation of information retrieval systems is one of the core tasks in information retrieval. Problems include the inability to exhaustively label all documents for a topic, generalizability from a small number of topics, and incorporating the variability of retrieval systems. Previous work addresses the evaluation of systems, the ranking of queries by difficulty, and the ranking of individual retrievals by performance. Approaches exist for the case of few and even no relevance judgments. Our focus is on zero-judgment performance prediction of individual retrievals. One common shortcoming of previous techniques is the assumption of uncorrelated document scores and judgments. If documents are embedded in a high-dimensional space (as they often are), we can apply techniques from spatial data analysis to detect correlations between document scores. We find that the low correlation between scores of topically close documents often implies a poor retrieval performance. When compared to a state of the art baseline, we demonstrate that the spatial analysis of retrieval scores provides significantly better prediction performance. These new predictors can also be incorporated with classic predictors to improve performance further. We also describe the first large-scale experiment to evaluate zero-judgment performance prediction for a massive number of retrieval systems over a variety collections in several languages.
- J. Aslam and V. Pavlu. Query hardness estimation using jensen-shannon divergence among multiple scoring functions. In ECIR 2007: Proceedings of the 29th European Conference on Information Retrieval, 2007. Google ScholarDigital Library
- J. A. Aslam, V. Pavlu, and E. Yilmaz. A statistical method for system evaluation using incomplete judgments. In S. Dumais, E. N. Efthimiadis, D. Hawking, and K. Jarvelin, editors, Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 541--548. ACM Press, August 2006. Google ScholarDigital Library
- D. Carmel, E. Yom-Tov, A. Darlow, and D. Pelleg. What makes a query difficult? In SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 390--397, New York, NY, USA, 2006. ACM Press. Google ScholarDigital Library
- B. Carterette, J. Allan, and R. Sitaraman. Minimal test collections for retrieval evaluation. In SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 268--275, New York, NY, USA, 2006. ACM Press. Google ScholarDigital Library
- A. D. Cliff and J. K. Ord. Spatial Autocorrelation. Pion Ltd., 1973.Google Scholar
- M. Connell, A. Feng, G. Kumaran, H. Raghavan, C. Shah, and J. Allan. Umass at tdt 2004. Technical Report CIIR Technical Report IR -- 357, Department of Computer Science, University of Massachusetts, 2004.Google Scholar
- S. Cronen-Townsend, Y. Zhou, and W. B. Croft. Precision prediction based on ranked list coherence. Inf. Retr., 9(6):723--755, 2006. Google ScholarDigital Library
- F. Diaz. Regularizing ad hoc retrieval scores. In CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management, pages 672--679, New York, NY, USA, 2005. ACM Press. Google ScholarDigital Library
- F. Diaz and R. Jones. Using temporal profiles of queries for precision prediction. In SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pages 18--24, New York, NY, USA, 2004. ACM Press. Google ScholarDigital Library
- D. A. Griffith. Spatial Autocorrelation and Spatial Filtering. Springer Verlag, 2003.Google ScholarCross Ref
- B. He and I. Ounis. Inferring Query Performance Using Pre-retrieval Predictors. In The Eleventh Symposium on String Processing and Information Retrieval (SPIRE), 2004.Google Scholar
- N. Jardine and C. J. V. Rijsbergen. The use of hierarchic clustering in information retrieval. Information Storage and Retrieval, 7:217--240, 1971.Google ScholarCross Ref
- D. Jensen and J. Neville. Linkage and autocorrelation cause feature selection bias in relational learning. In ICML '02: Proceedings of the Nineteenth International Conference on Machine Learning, pages 259--266, San Francisco, CA, USA, 2002. Morgan Kaufmann Publishers Inc. Google ScholarDigital Library
- O. Kurland and L. Lee. Corpus structure, language models, and ad hoc information retrieval. In SIGIR '04: Proceedings of the 27th annual international conference on Research and development in information retrieval, pages 194--201, New York, NY, USA, 2004. ACM Press. Google ScholarDigital Library
- M. Montague and J. A. Aslam. Relevance score normalization for metasearch. In CIKM '01: Proceedings of the tenth international conference on Information and knowledge management, pages 427--433, New York, NY, USA, 2001. ACM Press. Google ScholarDigital Library
- T. Qin, T.-Y. Liu, X.-D. Zhang, Z. Chen, and W.-Y. Ma. A study of relevance propagation for web search. In SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 408--415, New York, NY, USA, 2005. ACM Press. Google ScholarDigital Library
- I. Soboroff, C. Nicholas, and P. Cahan. Ranking retrieval systems without relevance judgments. In SIGIR '01: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pages 66--73, New York, NY, USA, 2001. ACM Press. Google ScholarDigital Library
- V. Vinay, I. J. Cox, N. Milic-Frayling, and K. Wood. On ranking the effectiveness of searches. In SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 398--404, New York, NY, USA, 2006. ACM Press. Google ScholarDigital Library
- Y. Zhou and W. B. Croft. Ranking robustness: a novel framework to predict query performance. In CIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge management, pages 567--574, New York, NY, USA, 2006. ACM Press. Google ScholarDigital Library
Index Terms
- Performance prediction using spatial autocorrelation
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