## 1 Introduction
To optimize IPS, researchers commonly apply smoothing techniques and regularization methods [9] to reduce the high variance introduced by IPS, thereby improving model robustness and accuracy. IPS aligns the distribution of offline data with the target learning objective by adjusting sample weights [16]. While existing IPS-based debiasing methods improve recommendation performance compared to approaches ignoring bias, they exhibit significant limitations. These methods often assume the following: (1) selection bias remains static over time and (2) user preferences remain constant despite item usage. However, real-world user data reveal that both selection bias and user preferences are dynamic [8]. When they evolve over time [8], static methods fail to eliminate recommendation bias. To tackle this, researchers have defined dynamic debiasing [2]; as data change over time, models must continuously update to adapt to dynamically shifting user preferences and reflect current bias characteristics. Dynamic debiasing seeks models that explain user behavior across entire time spans, rather than relying solely on current behavior (while considering performance constraints). Previous work, however, assumes static selection bias and only models dynamic user preferences, leading to suboptimal debiasing performance. To address this, some studies enhance factor-based collaborative filtering algorithms by incorporating temporal dynamics [17], arguing that temporal models outperform static ones. [PERSON] et al. [8] theoretically proved that existing debiasing methods become biased when both selection bias and user preferences are dynamic, and introduced a debiasing model under this assumption. A key limitation, however, is that existing collaborative filtering algorithms are static models [18], assuming fixed relationships across time. Moreover, all current IPS-based debiasing studies rely on static IPS methods, which poorly adapt to dynamic scenarios, drastically reducing debiasing effectiveness. Therefore, this work proposes dynamic scene modeling, constructing a dynamic debiasing module that incorporates time-aware inverse propensity scoring to capture dynamically changing user preferences and alleviate the high variance issue of IPS. Therefore, we propose the design of a debiased recommendation framework (sentiment classification and temporal dynamic debiased recommendation module (SCTD)) on the basis of sentiment analysis and temporal dynamic debiasing. Specifically, the SCTD consists of a sentiment classification scoring module (sentiment classification scoring module, SCS) and a temporal dynamic debiased recommendation module (temporal dynamic debiased recommendation module, TDR). The main contributions of this paper are as follows:  
1. We propose a recommendation framework based on sentiment bias and temporal dynamic debiasing, namely the SCTD, which utilizes the sentiment scores of reviews and ratings to better capture the user's true preferences for recommendation. 2. We focus on capturing dynamic user interests and real-world user behaviors by alleviating data imbalance and sparsity issues through two modules, the SCS and TDR. 3. We conducted comparative experiments on the Yelp and Jingdong datasets. The experimental results show that our model is optimal in all metrics and has good debiasing performance in the case of sparse data. Meanwhile, it shows that our model is of great significance in alleviating data sparsity in the real world, and provides a concrete implementation solution for solving the problem of dynamic bias in user behavior.