Online learning to rank in a listwise approach for information retrieval
- Publication Type:
- Conference Proceeding
- Citation:
- Proceedings - IEEE International Conference on Multimedia and Expo, 2019, 2019-July pp. 1030 - 1035
- Issue Date:
- 2019-07-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Online Learning to Rank in a Listwise Approach for Information Retrieval.pdf | Published version | 215.22 kB |
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© 2019 IEEE. A common approach to learning to rank is to minimize the pair-wise loss. However, established analysis shows that pair-wise loss does not necessarily lead to an optimal list-wise ranking measures, e.g., average precision (AP) or area under precision-recall curve (AUPRC). It becomes more difficult in the online learning setting, where the data arrives sequentially and is scanned only once. This paper proposes an online learning-to-rank algorithm by minimizing the list-wise ranking error, which achieves a vanishing gap between the list-wise loss and the ranking measures. Experiments also testify the effectiveness and robustness of the proposed online List-wise algorithm.
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