Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space

CIKM '20: The 29th ACM International Conference on Information and Knowledge Management Virtual Event Ireland October, 2020, pp. 2605-2612, 2020.

Cited by: 0|Bibtex|Views7|DOI:https://doi.org/10.1145/3340531.3412713
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Abstract:

Traditional Learning to Rank (LTR) models in E-commerce are usually trained on logged data from a single domain. However, data may come from multiple domains, such as hundreds of countries in international E-commerce platforms. Learning a single ranking function obscures domain differences, while learning multiple functions for each domai...More

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