A social recommendation model based on social semantic mining and denoising

In the era of information technology, recommendation systems play a crucial role in information filtering and user preference identification. Notably, the auxiliary information provided by online social platforms offers significant support for enhancing the performance of recommendation systems. Based on the hypothesis that socially connected users share similar preferences, integrating social relationships as supplementary information into recommendation algorithms can significantly enhance recommendation accuracy while effectively mitigating the cold-start problem. However, existing social recommendation systems primarily rely on explicit social relationships as auxiliary information, often overlooking the value of potential social connections. Research indicates that users with potential social links may also possess valuable preference information. We believe that mining potential social relationships can provide valuable auxiliary information, thereby enhancing the performance of recommendation systems. To address this issue, we propose a social recommendation model based on social semantic mining and denoising (SSMD). Specifically, we propose an encoder-decoder architecture to learn explicit social user representations and mine potential social relationships. Considering the potential noise in these implicit connections, we design a denoising module that utilizes user preference information to filter unreliable social links. Furthermore, we implement cross-view information alignment between the potential social graph and interaction graph through an auxiliary loss function. Extensive experiments conducted on multiple public datasets demonstrate that our SSMD method outperforms various baseline approaches with significant improvements.