# Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading
## 5 Conclusions  
In this paper, we first analyze the defects of existing spreading-based recommendation algorithms and discuss the application of graph neural networks in recommendation systems. Building on this foundation, we propose Light Graph Convolutional Hybrid Spreading (LGCNHS), a novel recommendation algorithm that integrates spreading-based recommendation algorithms with LightGCN. First, the model utilizes a features matrix for embedding optimization of users and items; second, the latent embedding representations of users and items are learned through a LightGCN; and finally, the learned embedding matrix is incorporated as a key parameter into a hybrid spreading algorithm for generating recommendation results. Comparative experiments and ablation experiments were conducted on two publicly available datasets, MovieLens, and Douban. The results demonstrate that the recommendation effect of LGCNHS has improved accuracy and diversity over related methods.  
While LGCNHS outperforms related algorithms and provides a novel perspective for spreading-based recommendation algorithms, there are still some potential limitations.  
Figure 5: (**A**) Ablation experiments in the dataset MovieLens (**B**) Ablation experiments in the dataset Douban.  
First, LGCNHS faces challenges regarding computational complexity and resource consumption when handling large-scale datasets, which poses a challenge to its scalability, optimizing computational efficiency in large-scale data environments remains a question worthy of in-depth research. In a large-scale data environment, how to optimize computational efficiency to enhance the practicality of algorithms remains an important issue that urgently needs to be addressed. Future research can focus on algorithm optimization, parallel processing, and other directions to tackle this challenge and improve the performance of LGCNHS in large-scale data scenarios.  
Additionally, the interpretability of LGCNHS is relatively weak, which may affect its transparency and user trust in practical applications to a certain extent. To enhance user acceptance and trust in the system, future research could explore ways to improve model interpretability, such as introducing interpretability frameworks, visualization techniques, or combining local model explanation methods, thereby providing users with clearer and more understandable reasons for recommendations.  
Finally, LGCNHS still has room for further improvement, particularly in the broader field of recommendation system. Future research could explore more specifically how to enhance the performance and adaptability of the model, for instance, by optimizing the recommendation process through reinforcement learning, allowing the recommendation system to achieve more personalized and precise results by continuously interacting with users and self-adjusting. Furthermore, alternative feature selection strategies are also a worthwhile direction for expansion, investigating how to optimize the model structure of LGCNHS through more efficient feature selection methods to improve its accuracy and generalization ability in specific application scenarios.  
We hope these issues can be effectively addressed to propose more innovative and practical recommendation algorithms in subsequent research, and we also hope this paper can provide inspiration for researchers and practitioners in related fields.  
Conceptualization, Y.D.; Methodology, Y.D.; Validation, Y.D.; Investigation, Y.D. and J.L.; Writing--original draft, Y.D.; writing--review and editing, L.Z. and X.L.; Supervision, L.Z. and X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 62202281.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.