# Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading
###### Abstract  
With the explosive growth of information on the internet, personalized recommendation technology has become an important tool for helping users efficiently acquire information. However, existing spreading-based recommendation algorithms only consider user choices and fail to fully leverage the potential relationships between users and items. Additionally, the incomplete utilization of user and item information limits their application potential and applicable scenarios, resulting in suboptimal recommendation performance in practical applications. To address this issue, we propose a Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading (LGCNHS). This algorithm first optimizes the embeddings of users and items using their respective feature matrix, then learns the latent embedding representations of users and items through a lightweight graph convolutional network. Finally, the latent embedding representations are incorporated as key parameters into the hybrid spreading recommendation algorithm to generate recommendations. Comparative experiments on two publicly available datasets, MovieLens and Douban, demonstrate that LGCNHS achieves improved accuracy and diversity in recommendations compared to related methods. The algorithm code is available on github. recommendation system; spreading-based recommendation; graph convolutional network +
Footnote †: journal: Applied sciences  
0000-0001-9998-4000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-000  
deep learning recommendation algorithms are powerful but require a large amount of labeled data, suffer from poor interpretability, and are prone to overfitting [3]. Facing the long-standing challenges of recommendation systems, some scholars have adopted a physics-based approach, modeling user-item interaction data as a bipartite network and leveraging tools like random walk, heat spreading, and heat conduction to design effective recommendation algorithms [4; 5; 6]. The integration of prior knowledge embedded in physical models with recommendation systems has now emerged as a new paradigm for exploring user recommendations. Among these, spreading-based recommendation algorithms are the most representative. These algorithms generate recommendations by assigning initial resources to items and spreading the resources through physical processes such as random walk and heat spreading [6]. Current research for spreading-based recommendation algorithms focuses on the way resources are updated and allocated during the spreading process [7; 8; 9; 10]. However, the core of this class of algorithms is to utilize the user-item interaction data to set the initial resource value of the item based on whether users have interacted with them. These values are then updated through the spreading process to generate recommendations. This approach ignores auxiliary information, such as user features and item features. Consequently, regardless of how the resource allocation method is modified, this spreading process still interprets user choices as an indication of interest in items and updates resources accordingly, ultimately generating recommendations based on the user's historical behaviors. Although this approach reflects the user's behavior to some extent, it fails to address the limitations of such algorithms, which do not fully exploit the potential relationships between users and items or leverage auxiliary information, such as user features and item features. This restricts their application potential and applicable scenarios, leading to suboptimal recommendation performance in real-world scenarios. Specifically, existing methods suffer from the following key limitations: (1) over-reliance on user behavior while neglecting personalized user features; (2) ineffective integration of latent item information, making it difficult to capture deep semantic associations between items; and (3) insufficient exploration of user-item interaction data, failing to fully account for the complex potential relationships between users and items.