The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Search result re-ranking by feedback control adjustment for time-sensitive query
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Next [Zhang et al. 2009] described a re-ranking score adjustment to give a ranking boost to fresh documents.”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Zhang et al. 2009
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cumulated gain-based evaluation of IR techniques
Järvelin, K., & Kekäläinen, J. (2002) · ACM Transactions on Information Systems · doi:10.1145/582415.582418 · Cited by 4,841
Flagged first, then by citations
Research works printed but not linked: 8
- Eugene Agichtein, Eric Brill, and Susan Dumais. 2006. Improving web search ranking by incorporating user behavior information. In SIGIR '06, pages 19-26.
- Fernando Diaz and Rosie Jones. 2004. Using temporal profiles of queries for precision prediction. In Proc. 27th Ann. Intl. ACM SIGIR Conf. on Research and De- velopment in Information Retrieval, pages 18-24, New York, NY, USA. ACM.
- Yoav Freund, Raj D. Iyer, Robert E. Schapire, and Yoram Singer. 1998. An efficient boosting algorithm for combining preferences. In ICML '98: Proceedings of the Fifteenth International Conference on Machine Learning, pages 170-178.
- Thorsten Joachims. 2002. Optimizing search engines using clickthrough data. In KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 133- 142.
- Xiaoyan Li and W. Bruce Croft. 2003. Time-based language models. In Proc. 12th Intl. Conf. on Infor- mation and Knowledge Management, pages 469-475, New York, NY, USA. ACM.
- Jaime Teevan, Susan T. Dumais, and Eric Horvitz. 2005. Personalizing search via automated analysis of inter- ests and activities. In SIGIR '05, pages 449-456.
- Chengxiang Zhai and John Lafferty. 2004. A study of smoothing methods for language models applied to in- formation retrieval. ACM Trans. Inf. Syst., 22(2):179- 214.
- Zhaohui Zheng, Keke Chen, Gordon Sun, and Hongyuan Zha. 2007. A regression framework for learning rank- ing functions using relative relevance judgments. In SIGIR '07, pages 287-294.
Cited by
Sorted by citations
Survey of Temporal Information Retrieval and Related Applications
Campos, R., Dias, G., Jorge, A. M., & Jatowt, A. (2015) · ACM Computing Surveys · doi:10.1145/2619088 · Cited by 262
Metadata
- Authors
- Zhang, R., Chang, Y., Zheng, Z., Metzler, D., & Nie, J.-Y.
- Venue
- Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers on - NAACL '09
- Published
- 2009
- Pages
- 165
- Cited by
- 11
- References
- 1
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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