A Graph based Recommendation Model for Short Video with Attention and Message Passing

The rise of the Internet has revolutionized the way information is accessed and consumed, leading to an era characterized by data abundance and complexity. In this landscape, users are often inundated with numerous choices, creating a challenge known as information overload. To address this challenge, recommendation systems have emerged as indispensable tools across various online platforms. These systems, by analyzing user attributes and behaviors, aim to provide personalized recommendations, thus assisting users in navigating the vast array of available information. However, conventional recommendation algorithms often overlook valuable user-item features embedded in additional data sources such as review texts. Moreover, integrating auxiliary information to enhance recommendation accuracy adds complexity to the process. In light of these considerations, this paper introduces an interest-aware message passing recommendation model. By partitioning the graph into subgraphs based on user and item interactions, the model leverages graph convolution operations to learn node representations, thereby enhancing the expression of users' interests and preferences. Through empirical evaluation on benchmark datasets, our model outperforms existing baselines, demonstrating its efficacy in addressing the challenges of recommendation in the age of data abundance.