## 2 Related Work
### Dynamic Debiased Recommendation System  
To address dynamic scene debiasing, three common methods are typically employed [2]: time window, time decay function, and integration-based learning. The use of time windows and time decay functions for the analysis of recent operations may result in the loss of pertinent information and the introduction of noise. Integration-based learning methods employ a variety of models, each of which considers only a fraction of the total behavioral spectrum. Current studies have improved recommendation accuracy by using multilayer perceptron [19], convolutional neural networks [20], or graph neural networks [21], but all of them ignored the effect of temporal variations on user preferences. To address these issues, one paper introduced a definition of dynamic debiasing [2]. It is essential to recognize that data are subject to change over time and therefore require constant updating with the latest modeling techniques to reflect their current nature. This approach assumes that selection bias is static and that only dynamic user preference scenarios are modeled, which results in the debiasing approach exhibiting suboptimal performance. In response to the aforementioned issue, some researchers have sought to enhance factor-based collaborative filtering algorithms by incorporating temporal features [17]. They proposed that temporal models are more effective than static models. However, a challenge persists, as existing collaborative filtering algorithms are static models, wherein relationships are assumed to be fixed at different times. Relational data, however, evolve over time, exhibiting strong temporal patterns. In a study presented at WSDM '22 [8], a theoretical assumption was made, and it was proven that existing debiasing methods are no longer unbiased when both selection bias and user preferences are dynamic.  
From the above, it follows that the problem we are currently facing is how to mitigate selection bias in recommendation tasks in dynamic scenarios. Currently, methods for solving the problem of selection bias include data filling (data imputation) and reverse tendency weighting (propensity score). The mainstream method for data imputation suffers from empirical errors due to improper designation of the missing data model or inaccurate estimation of the rating value. The performance of IPS-based models depends on the accuracy of their propensities. Therefore, previous studies have used heuristic optimization schemes such as directly populating missing values. These methods are prone to empirical errors due to improperly specified missing data models or inaccurate score estimation. Currently, the use of propensity scores in training recommendation models is a more common method to mitigate selection bias. The challenge with propensity scores is that they directly target IPS-based unbiased estimators and optimize specific losses. Despite this, formulating accurate propensity scores is a rigorous task, and the performance of IPS-based models depends on the accuracy of these scores. Propensity score-based methods have large variances and can lead to nonoptimal results.  
To summarize the above, the existing IPS-based dynamic debiasing methods are flawed in that (1) the research hypothesis identifies that the existing selection bias and user preference are static, but in reality both change over time, so the existing debiasing methods are no longer theoretically unbiased, and (2) the IPS-based methods are distorted because of the assumptions of the dynamic scenarios leading to the inability of the static IPS methods to eliminate the recommendation bias, and bring about the high variance problem affecting recommendation model performance. So, in order to improve the accuracy of IPS, we build the TDR module based on dynamic scene modeling to accommodate dynamically changing selection biases and user preferences.