Burger: Robust Graph Denoising-augmentation Fusion and Multi-semantic Modeling in Social Recommendation

TL;DR

Burger is a social recommendation model that enhances accuracy by integrating multi-semantic information, denoising social relations, and employing graph convolutional networks for better user-item and social preference modeling.

Contribution

The paper introduces a novel multi-semantic social recommendation framework with graph denoising-augmentation fusion, leveraging tensor and graph convolutional networks for improved social recommendation accuracy.

Findings

Outperforms state-of-the-art models on three real datasets.

Effectively models mutual semantic influence between social and user-item networks.

Reduces social noise using Bayesian posterior probability.

Abstract

In the era of rapid development of social media, social recommendation systems as hybrid recommendation systems have been widely applied. Existing methods capture interest similarity between users to filter out interest-irrelevant relations in social networks that inevitably decrease recommendation accuracy, however, limited research has a focus on the mutual influence of semantic information between the social network and the user-item interaction network for further improving social recommendation. To address these issues, we introduce a social \underline{r}ecommendation model with ro\underline{bu}st g\underline{r}aph denoisin\underline{g}-augmentation fusion and multi-s\underline{e}mantic Modeling(Burger). Specifically, we firstly propose to construct a social tensor in order to smooth the training process of the model. Then, a graph convolutional network and a tensor convolutional network are employed to capture user's item preference and social preference, respectively. Considering the different semantic information in the user-item interaction network and the social network, a bi-semantic coordination loss is proposed to model the mutual influence of semantic information. To alleviate the interference of interest-irrelevant relations on multi-semantic modeling, we further use Bayesian posterior probability to mine potential social relations to replace social noise. Finally, the sliding window mechanism is utilized to update the social tensor as the input for the next iteration. Extensive experiments on three real datasets show Burger has a superior performance compared with the state-of-the-art models.