The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Learning to Rank with Selection Bias in Personal Search
Abstract
Click-through data has proven to be a critical resource for improving search ranking quality. Though a large amount of click data can be easily collected by search engines, various biases make it difficult to fully leverage this type of data. In the past, many click models have been proposed and successfully used to estimate the relevance for individual query-document pairs in the context of web search. These click models typically require a large quantity of clicks for each individual pair and this makes them difficult to apply in systems where click data is highly sparse due to personalized corpora and information needs, e.g., personal search. In this paper, we study the problem of how to leverage sparse click data in personal search and introduce a novel selection bias problem and address it in the learning-to-rank framework. This paper proposes a few bias estimation methods, including a novel query-dependent one that captures queries with similar results and can successfully deal with sparse data. We empirically demonstrate that learning-to-rank that accounts for query-dependent selection bias yields significant improvements in search effectiveness through online experiments with one of the world's largest personal search engines.
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Cites
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Cited by
Bias 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
Self-citationUnbiased 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
Equity of Attention: Amortizing Individual Fairness in Rankings
Biega, A. J., Gummadi, K. P., & Weikum, G. (2018) · The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval · doi:10.1145/3209978.3210063 · Cited by 337
A General Framework for Counterfactual Learning-to-Rank
Agarwal, A., Takatsu, K., Zaitsev, I., & Joachims, T. (2019) · Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3331184.3331202 · Cited by 115
Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce
Wu, L., Hu, D., Hong, L., & Liu, H. (2018) · The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval · doi:10.1145/3209978.3209993 · Cited by 85
Metadata
- Authors
- Wang, X., Bendersky, M., Metzler, D., & Najork, M.
- Venue
- Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2016-07-07
- Pages
- 115-124
- OpenAlex
- W2340526403
- Cited by
- 284
- References
- 36
- FWCI
- 18.80
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wang, X., Bendersky, M., Metzler, D., & Najork, M. (2016). Learning to Rank with Selection Bias in Personal Search. Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. https://doi.org/10.1145/2911451.2911537
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