The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
A dynamic bayesian network click model for web search ranking
Abstract
As with any application of machine learning, web search ranking requires labeled data. The labels usually come in the form of relevance assessments made by editors. Click logs can also provide an important source of implicit feedback and can be used as a cheap proxy for editorial labels. The main difficulty however comes from the so called position bias - urls appearing in lower positions are less likely to be clicked even if they are relevant. In this paper, we propose a Dynamic Bayesian Network which aims at providing us with unbiased estimation of the relevance from the click logs. Experiments show that the proposed click model outperforms other existing click models in predicting both click-through rate and relevance.
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Cites
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Research works printed but not linked: 4
- Z. Ghahramani . Learning dynamic bayesian network . In C. L. Giles and M. Gori, editors, Adaptive processing of temporal information, Lecture notes in artificial intelligence . Springer-Verlag , 1998 . Z. Ghahramani. Learning dynamic bayesian network. In C. L. Giles and M. Gori, editors, Adaptive processing of temporal information, Lecture notes in artificial intelligence. Springer-Verlag, 1998.
- 10.1145/1242572.1242643 · DOI · Paper page
- 10.5555/188490.188561 · doi:10.5555/188490.188561
- D. Zhou , L. Bolelli , J. Li , C. L. Giles , and H. Zha . Learning user clicks in web search . In International Joint Conference on Artificial Intelligence (IJCAI07) , 2007 . D. Zhou, L. Bolelli, J. Li, C. L. Giles, and H. Zha. Learning user clicks in web search. In International Joint Conference on Artificial Intelligence (IJCAI07), 2007.
Cited by
Expected reciprocal rank for graded relevance
Chapelle, O., Metlzer, D., Zhang, Y., & Grinspan, P. (2009) · Proceedings of the 18th ACM conference on Information and knowledge management · doi:10.1145/1645953.1646033 · Cited by 832
Self-citationBias and Debias in Recommender System: A Survey and Future Directions
Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., & He, X. (2023) · ACM Transactions on Information Systems · doi:10.1145/3564284 · Cited by 741
Unbiased Learning-to-Rank with Biased Feedback
Joachims, T., Swaminathan, A., & Schnabel, T. (2017) · Proceedings of the Tenth ACM International Conference on Web Search and Data Mining · doi:10.1145/3018661.3018699 · Cited by 531
Baeza-Yates, R. (2018) · Communications of the ACM · doi:10.1145/3209581 · Cited by 432
Chuklin, A., Markov, I., & de Rijke, M. (2015) · Synthesis Lectures on Information Concepts, Retrieval, and Services · doi:10.1007/978-3-031-02294-4 · Cited by 398
Metadata
- Authors
- Chapelle, O., & Zhang, Y.
- Venue
- Proceedings of the 18th international conference on World wide web
- Publisher
- ACM
- Published
- 2009-04-20
- Pages
- 1-10
- OpenAlex
- W2099213975
- Cited by
- 537
- References
- 23
- FWCI
- 27.39
- Type
- conference paper
- Language
- en
- Open access
- closed
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