부산대학교 도서관

With the advancement of the Internet, Graph Neural Networks based recommendation systems have become a topic of great concern in the research field. However, the current recommendation systems still have the following problems. First, it focuses on modeling users but ignores the problems of missing values of item rating vectors and non-corresponding positions of item rating vectors in the process of solving associated items; second, it focuses on the association relationship between users but pays less attention to the association relationship between items; third, there is insufficient research on the short-term attractiveness of items and the users' temporary preferences. To address the above problems, this study proposes the following solutions to better construct the item social graph and extract the short-term interest/attraction of users/items. Firstly, for problem one, this study reconstructs the item rating vector innovatively based on whether users have interaction with the items; secondly, for problem two, this study proposes to use Pearson similarity to calculate the association relationship between items so as to construct the item social graph. Again, for problem three, This paper investigates temporal information features and extracts short-term user preferences and item attractiveness. To achieve this, an attention network that focuses on temporal information features is constructed by combining channel attention and bidirectional long-term and short-term memory networks. Finally, it involves using a multilayer perceptron with a residual connection structure to combine user and item factors, leading to more accurate predictions. In this study, two publicly available datasets, Epinions and Ciao, were used in a comparative experiment. This model outperformed other baseline models in the experiment, resulting in a reduction of 1.42% and 1.24% in MAE values, and 1.47% and 1.38% in RMSE values, respectively. These findings suggest that incorporating short-term preferences and reconstructing item social graphs can enhance the precision of social recommendations.