Online Learning to Rank in a Listwise Approach for Information Retrieval
Fan Ma, Haoyun Yang, Haibing Yin, Xiaofeng Huang, Chenggang Clarence Yan, Xiang Zhi Meng · 2019
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.