Abstract
Although, semi-supervised learning with a small amount of labeled data can be utilized to improve the effectiveness of learning to rank in information retrieval, the pseudo labels created by semi-supervised learning may not reliable. The uncertain data nearby the boundaries of relevant and irrelevant documents for a given query has a significant impact on the effectiveness of learning to rank. Therefore, how to utilize the uncertain data to bring benefit for semi-supervised learning to rank is an excellent challenge. In this paper, we propose a semi-supervised learning to rank algorithm, that builds a query-quality predictor by utilizing uncertain data. Specially, this approach selects the training queries following the empirical observation that the relevant documents of high quality training queries are highly coherent. This approach learns from the uncertain data to predict the retrieval performance gain of a given training query by making use of query features. Then the pseudo labels for learning to rank are aggregated iteratively by semi-supervised learning with the selected queries. Experimental results on the standard LETOR dataset show that our proposed approaches outperform the strong baselines.
Supported by the Natural Science Foundation of Hefei University (18ZR07ZDA, 19ZR04ZDA), National Nature Science Foundation of China (Grant No. 61806068), the natural science research key project of Anhui university (Grant No. KJ2018A0556), the grant of Natural Science Foundation of Hefei University (Grant No. 16-17RC19,18-19RC27).
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Acknowledgements
This work is supported in part by the Natural Science Foundation of Hefei University (18ZR07ZDA,19ZR04ZDA), National Nature Science Foundation of China (Grant No. 61806068), the natural science research key project of Anhui university (Grant No. KJ2018A0556), the grant of Natural Science Foundation of Hefei University (Grant No. 16-17RC19,18-19RC27).
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Zhang, X., Zhao, Z., Liu, C., Zhang, C., Cheng, Z. (2019). Semi-supervised Learning to Rank with Uncertain Data. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_4
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