Addressing the challenges faced by current cold start recommendation models in effectively mining structural and semantic information in heterogeneous information networks, and their tendency to overlook user behavior attributes within these networks, this paper introduces a Meta-Learning-based Multi-View Contrast Fusion Cold Start Recommendation Algorithm (MVC-ML) . This algorithm effectively tackles the cold start problem at both the model and data layers. Within the MVC-ML framework, it firstly extracts higher-order semantic information from heterogeneous information networks using a meta-path view. Subsequently, it captures the network's structural features using a network pattern view. Following this, the algorithm analyzes user behavior attribute information through a clustering view. Finally, MVC-ML employs a contrast learning method to integrate the information extracted from these three views, thus generating accurate representation vectors. Experimental validations on datasets, including DBook, demonstrate that the MVC-ML model, in a cold start scenario, reduces MAE by 1.67%, lowers RMSE by 2.06%, and increases nDCG@K by 1.48% compared to traditional heterogeneous information network models such as MetaHIN. These results fully confirm the rationality and effectiveness of the MVC-ML algorithm.

[2024-01-24] Accepted Paper /doc/2023.11.0547/v1