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Sarvi et al., 2023 - Google Patents

On the impact of outlier bias on user clicks

Sarvi et al., 2023

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Document ID
5436340214522945123
Author
Sarvi F
Vardasbi A
Aliannejadi M
Schelter S
de Rijke M
Publication year
Publication venue
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

External Links

Snippet

User interaction data is an important source of supervision in counterfactual learning to rank (CLTR). Such data suffers from presentation bias. Much work in unbiased learning to rank (ULTR) focuses on position bias, ie, items at higher ranks are more likely to be examined …
Continue reading at dl.acm.org (PDF) (other versions)

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