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Recommendation systems address content delivery system information overload. Recent recommender systems include knowledge-aware deep recommender models. These models train user and item embeddings using Graph Neural Networks and operate on collaborative knowledge graphs that combine user-item interactions with item-entity relationships to deliver more item information. Certain models employ a two-step learning procedure wherein the primary objective is to generate recommendations while simultaneously training knowledge graph embeddings. By co-learning, the models are able to comprehend the structural connections that exist between entities and relations, while also improving user embeddings through the use of previous encounters. The objective of this research endeavour is to assess the efficacy of cutting-edge models through the integration of knowledge graph embedding co-learning and the manipulation of embedding techniques. Co-learning boosts quicker learning and enhances the precision of recommendations, according to empirical findings. When a KG embedding layer was incorporated into KGCN, the recall metric outperformed the initial model by as much as 37%. Compared to translational embedding techniques, RotatE decreased training duration by 18% and demonstrated a noteworthy 15% improvement in the recall metric. The study's outcome offer valuable guidance for enhancing recommendation accuracy and reducing training time for models incorporating Knowledge Graph (KG) embeddings. Furthermore, the study introduces a new application for complex-valued KG embeddings. [ABSTRACT FROM AUTHOR]

This study aims to explore the application and innovation of deep learning in film and television interaction in the context of artificial intelligence. By analyzing the interaction needs of users with films and television, the Knowledge Graph Convolutional Network (KGCN) algorithm is introduced, taking into account both user interests and the associated information between films and television. A film recommendation model based on KGCN fused with user interests is proposed. This model extensively explores the relational and semantic information between film works to provide users with richer and more diverse recommendation content, meeting their diverse viewing needs and interactions. Through evaluating the model performance, the proposed model exhibits lower loss function values in terms of convergence. The model reaches a basic stable state with a loss function value of around 0.40 after 43 iterations, far superior to the baseline algorithm Convolutional Neural Network (CNN). In terms of accuracy, the proposed model achieves an Accuracy value of 96.53%, representing an improvement of at least 4.80%. Moreover, the precision and F1 values of the prediction accuracy are improved by over 4%. Additionally, in terms of Area Under the Curve (AUC) value, the proposed model also demonstrates a significant advantage, reaching a level of 98.05%. Therefore, the film recommendation model proposed in this study, which is based on KGCN fused with user interests, possesses significant advantages in film recommendation tasks, providing strong reference and guidance for the improvement and optimization of film recommendation systems.

Abstract Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the power of GCN to benefit various users from chemists to cheminformaticians, an open-source GCN tool, kGCN, is introduced. To support the users with various levels of programming skills, kGCN includes three interfaces: a graphical user interface (GUI) employing KNIME for users with limited programming skills such as chemists, as well as command-line and Python library interfaces for users with advanced programming skills such as cheminformaticians. To support the three steps required for building a prediction model, i.e., pre-processing, model tuning, and interpretation of results, kGCN includes functions of typical pre-processing, Bayesian optimization for automatic model tuning, and visualization of the atomic contribution to prediction for interpretation of results. kGCN supports three types of approaches, single-task, multi-task, and multi-modal predictions. The prediction of compound-protein interaction for four matrixmetalloproteases, MMP-3, -9, -12 and -13, in the inhibition assays is performed as a representative case study using kGCN. Additionally, kGCN provides the visualization of atomic contributions to the prediction. Such visualization is useful for the validation of the prediction models and the design of molecules based on the prediction model, realizing “explainable AI” for understanding the factors affecting AI prediction. kGCN is available at https://github.com/clinfo .

Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the power of GCN to benefit various users from chemists to cheminformaticians, an open-source GCN tool, kGCN, is introduced. To support the users with various levels of programming skills, kGCN includes three interfaces: a graphical user interface (GUI) employing KNIME for users with limited programming skills such as chemists, as well as command-line and Python library interfaces for users with advanced programming skills such as cheminformaticians. To support the three steps required for building a prediction model, i.e., pre-processing, model tuning, and interpretation of results, kGCN includes functions of typical pre-processing, Bayesian optimization for automatic model tuning, and visualization of the atomic contribution to prediction for interpretation of results. kGCN supports three types of approaches, single-task, multi-task, and multi-modal predictions. The prediction of compound-protein interaction for four matrixmetalloproteases, MMP-3, -9, -12 and -13, in the inhibition assays is performed as a representative case study using kGCN. Additionally, kGCN provides the visualization of atomic contributions to the prediction. Such visualization is useful for the validation of the prediction models and the design of molecules based on the prediction model, realizing "explainable AI" for understanding the factors affecting AI prediction. kGCN is available at https://github.com/clinfo. [ABSTRACT FROM AUTHOR]