Unbiased Learning to Rank with Unbiased Propensity Estimation (1804.05938v2)

Abstract: Learning to rank with biased click data is a well-known challenge. A variety of methods has been explored to debias click data for learning to rank such as click models, result interleaving and, more recently, the unbiased learning-to-rank framework based on inverse propensity weighting. Despite their differences, most existing studies separate the estimation of click bias (namely the \textit{propensity model}) from the learning of ranking algorithms. To estimate click propensities, they either conduct online result randomization, which can negatively affect the user experience, or offline parameter estimation, which has special requirements for click data and is optimized for objectives (e.g. click likelihood) that are not directly related to the ranking performance of the system. In this work, we address those problems by unifying the learning of propensity models and ranking models. We find that the problem of estimating a propensity model from click data is a dual problem of unbiased learning to rank. Based on this observation, we propose a Dual Learning Algorithm (DLA) that jointly learns an unbiased ranker and an \textit{unbiased propensity model}. DLA is an automatic unbiased learning-to-rank framework as it directly learns unbiased ranking models from biased click data without any preprocessing. It can adapt to the change of bias distributions and is applicable to online learning. Our empirical experiments with synthetic and real-world data show that the models trained with DLA significantly outperformed the unbiased learning-to-rank algorithms based on result randomization and the models trained with relevance signals extracted by click models.

An Analysis of "Unbiased Learning to Rank with Unbiased Propensity Estimation"

The paper "Unbiased Learning to Rank with Unbiased Propensity Estimation" proposes a method to improve the learning-to-rank framework by avoiding biases inherent in click data. The authors introduce a Dual Learning Algorithm (DLA) to simultaneously learn unbiased ranking models and propensity models directly from biased click data. This represents a departure from previous practices where the estimation of click bias (propensity model) and learning of ranking algorithms were undertaken separately.

At the core of this paper is the challenge of learning to rank using click data that is susceptible to various biases, such as position bias. Traditional methods involve either offline parameter estimation and online result randomization to estimate click propensities. However, offline methods necessitate query-document pairs' repeated observations, which limits their applicability in certain domains like personal search. On the other hand, online randomization can negatively impact user experience due to the introduction of non-deterministic factors into search engine results.

The authors address these issues by introducing a novel framework where the estimation of the propensity model and the ranking model occur simultaneously. They build on the observation that estimating a propensity model from click data can be considered a dual problem of unbiased learning to rank. The Dual Learning Algorithm developed in this paper offers a pathway to train ranking models directly from biased click data without pre-processing requirements. It is adaptive to changes in bias distributions, thus promising better adaptability for online learning scenarios.

The authors validate their approach through extensive experimentation on both synthetic and real-world datasets. They compare their results against traditional unbiased learning-to-rank methods that rely on result randomization and standard algorithms using derived relevance signals from click models.

Empirical results demonstrate that DLA-trained models significantly outperform traditional unbiased learning-to-rank algorithms. Notably, these models show strong improvements over those trained with signals derived from click models like User Browsing Model (UBM) and Dynamic Bayesian Network Model (DBN). This is attributed to DLA's ability to conduct end-to-end optimization for unbiased learning to rank. It underscores the advantage of jointly training propensity models and ranking models in a complementary fashion, leveraging each to improve the other's estimation.

From a theoretical standpoint, the paper provides a robust analysis of inverse propensity weighting (IPW) and inverse relevance weighting (IRW), proving that DLA can converge to the global optima under specified conditions. The concavity of loss functions with respect to model parameters is pivotal to this analytical framework.

The dual learning approach offers several practical advantages: it is efficient, avoids disrupting user experiences with online randomization, and is adaptable to changes in user behavior. This adaptability is critical as it promises seamless integration into production systems where user behavior and search engine interfaces are constantly evolving.

The research points toward future work on extending the framework beyond position bias, considering other types of click biases, and possibly integrating more sophisticated model architectures. Furthermore, exploring the impact of integrating the joint model learning approach in larger, more varied datasets and different domains may yield additional insights into its robustness and scalability.

Overall, this paper contributes an essential advancement in learning to rank by synergizing propensity estimation with ranking model training—offering a more cohesive and effective method of addressing biases in click data, subsequently enhancing the relevance and accuracy of ranked search results.