# The Use of Attentive Knowledge Graph Perceptual Propagation for Improving Recommendations
## 6 Conclusions and Future Work  
To address the data sparsity and cold-start problems of recommendation models, this paper proposes a recommendation algorithm, AKGP, that incorporates attention and knowledge graph-aware propagation. User-item interaction information and a knowledge graph are used as inputs. Using the knowledge graph embedding method, fine-grained modeling and the extraction of contextual information, the interests of users are effectively mined. The weights of entities with different relationships are computed through attention networks and used as propagation factors to propagate information from the knowledge graph, in order to extend domain information on users and items. Domain information is aggregated and preference values predicted using a stratified sampling strategy to retain item information features while avoiding sample bias. Finally, an end-to-end recommendation framework is formed. In this study, we conducted comparative experiments on publicly available datasets for three different scenarios. The results show that the AKGP model achieves significant performance improvement in both CTR prediction and Top-K recommendation compared to the baseline algorithm. The ablation experiments further validated the effectiveness of this method.  
In addition, we applied the model to the product design data and historical maintenance records provided by the project partner of this paper (an automotive parts manufacturing company). The knowledge graph is used to store and organize data related to product design, and the entities include parts, materials, manufacturing processes, etc. Based on the product information, the recommendation system predicts the top 10 suitable materials, most of which meet the product requirements. This helps reduce costs and increase productivity. The knowledge graph can also store the historical maintenance records of the equipment, causes of failure and repair methods. The recommendation system analyzes the operating status of the equipment and predicts the top 10 possible failures, most of which match the situation in which the failure occurred. This helps companies quickly identify the cause of failures and reduce unplanned downtime.  
Although the recommendation effect of this paper is improved in mitigating the cold-start and data sparsity problems, the performance of the model is yet to be tested in the face of large-scale datasets. Secondly, if the knowledge graph embedding representation is used directly without any constraint, it will easily lead to noise penetration into the underlying information. In future work, we will set constraints for representation learning based on the correlation between nodes. We also plan to introduce other auxiliary information into the recommendation algorithm, such as user reviews and social networks.  
[PERSON] performed the experiments, contributed to the construction of the knowledge graph and the design of the model, executed a detailed analysis, and wrote certain sections of the manuscript. [PERSON] set the direction of the research, wrote certain sections of the manuscript, and performed the final corrections. All authors have read and agreed to the published version of the manuscript.  
Figure 9: The results of different propagation depths on two datasets. (**a**) Results on the Last-FM dataset. (**b**) Results on the Book-Crossing dataset.  
**Funding:** This research was funded by the Science and Technology Innovation 2030--Major Project of \"New Generation Artificial Intelligence\" granted by Ministry of Science and Technology, grant number 2020 AAA0109300.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to express their sincere gratitude to all the teachers and students who helped to successfully complete this study.  
**Conflicts of Interest:** The authors declare no conflict of interest.