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2025 | OriginalPaper | Chapter

PR-Rank: A Parameter Regression Approach for Learning-to-Rank Model Adaptation Without Target Domain Data

Authors : Takumi Ito, Atsuki Maruta, Makoto P. Kato, Sumio Fujita

Published in: Web Information Systems Engineering – WISE 2024

Publisher: Springer Nature Singapore

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Abstract

This paper addresses a problem of constructing a Learning-to-Rank (LtR) model tailored to a target domain without using any domain-specific queries and relevance judgements. Our proposed method, PR-Rank, incorporates domain features, which are represented in a real-valued vector and can be estimated by domain experts, for adapting LtR models. The key component in our method is a parameter regression model that learns to regress the optimal parameters of the LtR model from the domain features. This eliminates the need for access to users’ queries and relevance judgements in a target domain, which is often unavailable in new and emerging services. In our experiments, we compared the performance of the proposed method against a domain-agnostic method, using publicly available LtR datasets including OHSUMED, MQ2007/2008, TREC Web track, and MSLR. The results showed that our method could outperform the baseline model trained on a large amount of data without considering domain differences.

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next chapter Weighted Linear Regression with Optimized Gap for Learned Index

The code is available at https://github.com/kasys-lab/PR-Rank.

https://fasttext.cc/docs/en/language-identification.html.

http://www.odp.org/.

https://www.kaggle.com/datasets/lucmichalski/dmoz-bert-multiclass-web-classification-dataset/.

https://sourceforge.net/p/lemur/wiki/RankLib/.

The difference between the PR-Rank and ideal models was not tested since we did not expect that PR-Rank outperform the ideal model.

Bennett, P.N., Svore, K., Dumais, S.T.: Classification-enhanced ranking. In: WWW, pp. 111–120 (2010)

Chen, D., Xiong, Y., Yan, J., Xue, G.R., Wang, G., Chen, Z.: Knowledge transfer for cross domain learning to rank. Inf. Retrieval 13(3), 236–253 (2010)

Dato, D., MacAvaney, S., Nardini, F.M., Perego, R., Tonellotto, N.: The istella22 dataset: Bridging traditional and neural learning to rank evaluation. In: SIGIR, pp. 3099–3107 (2022)

Gao, J., et al.: Model adaptation via model interpolation and boosting for web search ranking. In: EMNLP, pp. 505–513 (2009)

Gao, W., Cai, P., Wong, K.F., Zhou, A.: Learning to rank only using training data from related domain. In: SIGIR, pp. 162–169 (2010)

Geng, X., Liu, T.Y., Qin, T., Arnold, A., Li, H., Shum, H.Y.: Query dependent ranking using k-nearest neighbor. In: SIGIR, pp. 115–122 (2008)

Ghanbari, E., Shakery, A.: Query-dependent learning to rank for cross-lingual information retrieval. Knowl. Inf. Syst. 59(3), 711–743 (2019)CrossRef

Goswami, P., Amini, M.R., Gaussier, E.: Transferring knowledge with source selection to learn IR functions on unlabeled collections. In: CIKM, pp. 2315–2320 (2013)

Guo, Q., Chen, W., Wan, H.: AOL4PS: a large-scale data set for personalized search. Data Intell. 3(4), 548–567 (2021)CrossRef

10.

Hashemi, H., Zhuang, Y., Kothur, S.S.R., Prasad, S., Meij, E., Bruce Croft, W.: Dense retrieval adaptation using target domain description. In: ICTIR, pp. 95–104 (2023)

11.

Liu, T.Y., et al.: Learning to rank for information retrieval. Found. Trends® Inf. Retrieval 3(3), 225–331 (2009)

12.

Long, B., Lamkhede, S., Vadrevu, S., Zhang, Y., Tseng, B.: A risk minimization framework for domain adaptation. In: CIKM, pp. 1347–1356 (2009)

13.

Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579–2605 (2008)

14.

MacAvaney, S., Macdonald, C., Ounis, I.: Reproducing personalised session search over the AOL query log. In: ECIR, pp. 627–640 (2022)

15.

Macdonald, C., Dinçer, B.T., Ounis, I.: Transferring learning to rank models for web search. In: ICTIR, pp. 41–50 (2015)

16.

MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)

17.

Ni, W., Huang, Y., Xie, M.: A query dependent approach to learning to rank for information retrieval. In: WAIM, pp. 262–269 (2008)

18.

Peng, J., Macdonald, C., Ounis, I.: Learning to select a ranking function. In: ECIR, pp. 114–126 (2010)

19.

Qin, T., Liu, T.Y.: Introducing LETOR 4.0 datasets (2013). http://arxiv.org/abs/1306.2597

20.

Qin, T., Liu, T.Y., Xu, J., Li, H.: LETOR: a benchmark collection for research on learning to rank for information retrieval. Inf. Retrieval 13(4), 346–374 (2010)CrossRef

21.

Robertson, S., Zaragoza, H.: The probabilistic relevance framework: BM25 and beyond. Found. Trends®Inf. Retrieval 3(4), 333–389 (2009)

22.

Senter, R., Smith, E.A.: Automated readability index. AMRL-TR. Aerospace Medical Research Laboratories, pp. 1–11 (1967)

23.

Sun, S., et al.: Few-Shot text ranking with meta adapted synthetic weak supervision. In: ACL, pp. 5030–5043 (2020)

24.

Wang, K., Thakur, N., Reimers, N., Gurevych, I.: GPL: generative pseudo labeling for unsupervised domain adaptation of dense retrieval. In: NAACL, pp. 2345–2360 (2022)

25.

Zhu, P., Hauff, C.: Unsupervised domain adaptation for question generation with domain data selection and self-training. In: NAACL, pp. 2388–2401 (2022)

Title: PR-Rank: A Parameter Regression Approach for Learning-to-Rank Model Adaptation Without Target Domain Data
Authors: Takumi Ito
Atsuki Maruta
Makoto P. Kato
Sumio Fujita
Publisher: Springer Nature Singapore
Book: Web Information Systems Engineering – WISE 2024
Print ISBN: 978-981-9605-72-9

Electronic ISBN: 978-981-9605-73-6

Copyright Year: 2025
DOI: https://doi.org/10.1007/978-981-96-0573-6_1

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