Knowledge Graph-based Recommender Systems (KGRS) have attracted significant attention due to their potential to enhance both recommendation accuracy and interpretability by leveraging structured relational knowledge. Despite the widespread use of reasoning paths as explanations, systematic evaluation of explanation quality across different KGRS paradigms, and its relationship with recommendation correctness, remains limited. This study provides a comprehensive empirical analysis of three representative KGRS paradigms, including path-based, embedding-based, and hybrid methods, focusing on multiple dimensions of explanation quality, including temporal relevance, popularity, diversity, semantic consistency, and faithfulness.

The analysis investigates differences in explanation characteristics across paradigms, revealing that models such as TMER achieve high recommendation accuracy through constrained path structures, whereas reinforcement learning-based models, including TPRec and PGPR, generate explanations with stronger lexical alignment to user review rationales. The study also examines explanation quality for correctly recommended (relevant) and incorrectly recommended (irrelevant) items. The results show that explanation-ground truth consistency metrics (Precision, Recall, and F1) exhibit greater disparities between correctly and incorrectly recommended items than other evaluation metrics. In RippleNet, the impact of ripple set size and explanation-oriented neighbor sampling strategies on recommendation performance and explanation quality is analyzed. Non-uniform sampling guided by temporal relevance, popularity, or diversity effectively shapes ripple sets to enhance explanation properties without significantly affecting overall recommendation accuracy.

The findings highlight trade-offs between recommendation accuracy and explanation quality, demonstrating that careful model design and sampling strategies can produce interpretable, user-aligned recommendations while maintaining high performance. These insights provide guidance for developing KGRS capable of delivering accurate predictions accompanied by semantically rich, temporally relevant, and user-preferred explanations, thereby improving transparency, trust, and user satisfaction in real-world applications.

Images possess the ability to convey a wide range of emotions, and extracting affective information from images is crucial for affect prediction systems. This process can be achieved through the application of machine learning algorithms. Categorical Emotion States (CES) and Dimensional Emotion Space (DES) are two typical models used for representing emotions. Moreover, the development of a mapping schema between these representations can contribute to benefit the research in AI and psychology. Consequently, this study focuses on investigating the feasibility of translating emotions from DES to CES. To accomplish this goal, relevant databases are identified and combined as the training data, and machine learning models, namely Naive Bayes, K-nearest Neighbors, and Decision Tree are employed to perform the classification task. The results indicate the superior performance of the K-nearest Neighbors classifier, exhibiting higher mean accuracy (60%) and low standard deviation (0.004) among all implemented classifiers. Overall, the translation from DES to CES offers several advantages, including a simplified and interpretable representation of emotions, as well as the provision of a common language for discussing and expressing emotions.