Prediction of the Positional Propensity Scores Based on Multi Task Learning

Users’ click data distribution during search is quite different in different search scenarios.The existing methods such as CPBM (contextual position based model) only predict the positional propensity score in multiple scenarios through single model, which inevitably reduces the prediction accuracy in different scenarios and affects the effect of removing position bias. In this work, A MCPBM (multi-gate contextual position based model) based on multi-task learning is proposed. In this model, the information filtering structure is added to CPBM model to solve the problem of poor prediction accuracy during joint training on multi-scene data. At the same time, in order to alleviate the problem that the convergence speed of different tasks is inconsistent. We propose an exponentially weighted average dynamic adjustment algorithm, which speeds up MCPBM training and improves the overall prediction performance of MCPBM. The experimental results show that MCPBM model proposed in this paper is better than traditional CPBM model in prediction accuracy when multi-scene data is jointly trained. After using MCPBM model to remove the position bias in the training data , the ranking model obtained by training on the generated unbiased data promotes the AvgRank ranking metric of test data by 1%–5%.