## 2 Related Work
### Sentiment Bias  
Sentiment bias refers to the phenomenon where users' evaluations or ratings deviate from their true feelings due to personal emotions, preferences, or external factors (such as mood fluctuations or social influences). This bias directly impacts the accuracy and fairness of recommendation systems. Therefore, researching how to identify and eliminate sentiment bias from evaluation and review-based models has become a critical topic in the field of recommendation systems [22]. Currently, research on sentiment bias primarily focuses on the following aspects:  
1. Sentiment Analysis and Bias Identification: Using natural language processing (NLP) techniques to analyze user reviews and identify sentiment tendencies and biases. For example, sentiment lexicons, machine learning models (such as SVM or random forests), or deep learning models [23] (such as BERT) are employed to quantify the sentiment intensity in reviews and detect potential over-positive or over-negative biases. 2. Debiasing Model Construction: Introducing debiasing mechanisms into recommendation models to mitigate the impact of sentiment bias on recommendation results. For instance, causal inference-based methods [24] can separate user emotions from true preferences, thereby generating more objective recommendations. Additionally, some studies attempt to reduce the interference of sentiment bias through adversarial training [25] or multi-task learning [26]. 3. Fairness and Transparency Research: Sentiment bias not only affects recommendation performance but may also lead to unfair recommendations. Therefore, researchers are exploring how to balance fairness and transparency in the debiasing process. For example, explainable AI [27] techniques can be used to show users how recommendations are generated and ensure that different user groups are treated fairly. Sentiment bias is one of the critical factors affecting the performance of recommendation systems. By gaining a deeper understanding of the current research status of evaluation and review-based models and integrating advanced sentiment analysis and debiasing techniques, the accuracy and fairness of recommendation systems can be significantly improved, providing users with more reliable recommendation services. Among the methods employed by various recommendation systems, evaluation and rating-based recommendation models have garnered significant attention due to their ability to leverage explicit user feedback. These models primarily rely on user-provided ratings and reviews to predict preferences and suggest relevant items. In general, a rating-based recommendation model describes the attributes and connections between different users and items through a user-item rating matrix in the recommendation system. In the context of large datasets, the user-item rating matrix frequently exhibits characteristics of sparsity and imbalance, a consequence of the selective expression of user preferences. This phenomenon imposes limitations on rating-based models, rendering them susceptible to the cold-start problem and the challenges posed by data sparsity [28]. An area of focus is the incorporation of contextual information into rating-based models. Researchers are increasingly exploring ways to integrate temporal, spatial, and social context to improve recommendation accuracy. For example, temporal dynamics in user preferences are being modeled using recurrent neural networks (RNNs) or attention mechanisms, enabling systems to adapt to evolving user behaviors. In review-based recommendation models, user preferences are usually inferred by methods such as text analysis [29] and sentiment classification [30], and relevant content is recommended based on the user's review description of the item. A significant direction is the development of hybrid models that combine collaborative filtering (CF) with review-based insights. Traditional CF methods, which rely on user-item interaction matrices, often struggle with data sparsity and cold-start problems.