Most approaches used in social recommender systems concentrate mostly on positive relationships within the social network, frequently ignoring the insightful information that negative relationships can offer. A more thorough understanding of user characteristics and behaviors within a social network can result from integrating positive and negative links to construct a signed graph. However, there are two major challenges in developing signed social recommender systems. Considering negative links in these systems needs to be done carefully to make sure that these relationships are appropriately represented and used. Moreover, it is difficult to predict user interactions and preferences effectively when relationships represented by positive and negative links conflict, known as social inconsistency. Therefore, working with signed graphs requires the management of social inconsistency. To address these issues, in this paper, a signed social recommender system called SiSRS is presented using a deep architecture combined with network representation learning. The SiSRS is composed of three principal components. First, the application of signed graph attention networks is explored to collate and disseminate information across the network via graph motifs, leading to the creation of node embeddings. Second, a deep autoencoder model is employed to assimilate top-k semantic signed social data within the deep learning framework. Third, a novel loss function is formulated to reduce the impact of social inconsistency. The efficacy of the SiSRS model was evaluated through extensive experiments conducted on two datasets in terms of various evaluation metrics. The results indicated a superior performance of this approach compared to existing state-of-the-art methods.
