Understanding Bias and Variance of Learning-to-Rank Algorithms: An Empirical Framework
Learning-to-rank (LtR) algorithms are at the heart of modern day information retrieval systems. While a good number of LtR algorithms have been developed and scrutinized over the past decade, theoretical underpinnings of these algorithms are not thoroughly investigated so far. Amongst the theoretica...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2021.2009164 |