Lightweight Graph Convolutional Collaborative Filtering Recommendation Approach Incorporating Social Relationships

Graph convolutional network (GCN) has rapidly developed in various fields due to its powerful modeling capability. However, most of the researches directly inherit the complex design of GCN, such as feature transformation and nonlinear activation, which lacks thorough ablation analysis on GCN. In addition, implicit feedback is not fully utilized and data sparsity is not well resolved, which are also shortcomings of current recommendation algorithms. To solve the above problems, this paper proposes a lightweight graph convolutional collaborative filtering (F-LightGCCF) recommendation approach incorporating social relationships. Firstly, it abandons the design of feature transformation and nonlinear activation in graph convolutional models and simplifies model training. Additionally, a series of intermediate feedback from users’ implicit negative feedback is generated by taking advantage of social networks, which improves the utilization of implicit negative feedback. Secondly, it can model the long-range dependencies between users and items by using the dual attention mechanism, aggregating the contribution values of neighboring nodes and the importance of the learning vectors in each layer of the graph convolution layer respectively. Lastly, the inner product operation is used to obtain the association score between users and items. Extensive experiment results on two real-world datasets show that F-LightGCCF outperforms existing state-of-the-art recommendation methods. Further ablation studies and analyses validate the efficiency and effectiveness of the F-LightGCCF model.