基于相关修正的无偏排序学习方法

Unbiased Learning to Rank Based on Relevance Correction

用户点击数据较文档的相关标签更易被获取且能反映用户兴趣,将其作为标签能够有效降低人工标注成本并且模型能随数据实时更新.但用户点击含有偏差和噪声,因此需设计有效的无偏排序方法.针对无偏排序中对偶学习方法收敛得到次优解从而无法完全消除偏差的问题,提出一种基于相关修正的无偏排序学习方法.首先,利用现有小规模相关标注数据训练排序模型,对候选文档进行较精准的相关得分预测;再基于用户点击和文档相关得分训练点击倾向模型;最后,将得到的模型参数设为对偶去偏初始值并联合训练.该方法不影响模型上线的计算速度,可用于在线学习场景,模拟不同程度偏差噪声并在真实点击场景下进行测试,结果表明该方案能够有效提升现有无偏排序学习方法表现.

Compared with the human annotated relevance labels,the user click data are easily obtained and can better reflect user preferences.Using clicks as training labels can reduce the cost,and the ranking models can be updated in real time.However,the raw clicks are biased and noisy,so it is necessary to design an effective method of unbiased learning to rank.Aiming at the problem that the dual learning algorithm achieve sub-optimal solutions thus cannot eliminate the bias completely,we propose a new method of unbiased learning to rank based on relevance correction.Firstly,we use the existing small-scale query-document pairs with relevance labels to train the ranking model and then use it to get more accurate predictions of the relevance score.Secondly,the click data and the predicted relevance scores are used to train the propensity model.Finally,we take the parameter values of the obtained model as the initial values of the dual learning process,and then jointly train the models with user clicks.The proposed method does not affect the online calculation speed and can be used in online learning scenarios.Tested in different degrees of click bias and real click scenarios,the proposed method can enhance the performance of the existing method as showed in the results.