Recommendation systems have become an indispensable part of daily life. Social recommendation systems, which utilize social relationships and past behaviors to infer users’ preferences, have gained popularity in recent years. Exploring the inherent characteristics implied by higher-order relationships offers a new approach to social recommendation. However, it is challenging due to sparse social networks, influence heterogeneity, and noisy feedback. In this article, we propose a Heterogeneous Meta-path Graph Learning model for Higher-order Social Recommendation (HEAL). Within HEAL, we introduce a heterogeneous graph in social recommendation and utilize a meta-path-guided random walk to generate higher-order relationships. By encoding higher-order structures and semantics along different meta-graphs, HEAL can mitigate the limitation of data sparsity. Moreover, HEAL exploits aspect-aware and semantic-aware attentions to adaptively propagate and aggregate useful features from different meta-neighbors and higher-order relations. These attention-based aggregation layers allow HEAL to suppress the heterogeneity of social influences. Furthermore, HEAL adopts contrastive learning as a supplemental task to the recommendation task by maximizing the consistency between the self-discriminating objectives. This auxiliary task enables the model to learn more differentiated representations, further reducing its sensitivity to noisy feedback. We evaluate the performance of HEAL through extensive experiments on public datasets. The results demonstrate that leveraging higher-order relations can enhance the quality of social recommendations by better capturing the complexity and diversity of users’ preferences and interactions.

Traditional recommendation algorithms based on collaborative filtering suffer from a data sparsity problem. The emergence of online social network has enriched the user’s information, realizing a new way to solve recommendation tasks. Social-aware recommendation algorithms can effectively alleviate the data sparsity problem and improve the performance of recommendation systems. Despite the success of these algorithms, they have some common limitations. Most algorithms assume that social networks are homogeneous, with similar preferences among connected users. However, users may only share similar preferences in some aspects. Besides, different friends affect the user’s preference in different levels. And this influence of friends on users’ preference should be adaptive. Even close friends may have different influences in different decision-making processes. For example, a user may trust a friend in "travel" but distrust this friend in "music" because this friend had more travel experiences. Motivated by the above limitations, we designed a neural network model called adaptive attention neural network for social recommendation (ANSR) to study the interaction between a user and his or her social friends as well as infer the complex influence of the user’s social relationships on the user’s preferences. By utilizing the coattention mechanism, we can not only extract the user’s special attention to certain aspects of their friends but also determine the adaptive influences of different friends on the user. When the user interacts with different items, different attention weights will be assigned to the user and his or her friends, respectively. In addition, we also utilize network embedding to learn more efficient features of each user and incorporate these features into the ANSR to enhance the recommendation results. Moreover, we also conduct extensive experiments on four different real-world datasets and demonstrate that our proposed method performs better on all datasets compared with the state-of-the-art baseline methods.

Social recommendations typically utilize social relationships and past behaviors to predict users’ preferences. In real-world scenarios, the connections between users and items often extend beyond simple pairwise relationships. Leveraging hypergraphs to capture high-order relationships provides a novel perspective to social recommendation. However, effectively modeling these high-order relationships is challenging due to limited external knowledge and noisy feedback. To tackle these challenges, we propose a novel framework called self-supervised hypergraph learning for knowledge-aware social recommendation (SHLKR). In SHLKR, we incorporated three main types of connections: behavior, social, and attribute context relationships. These dependencies serve as the basis for defining hyperedges in the hypergraphs. A dual-channel hypergraph structure is created based on these relationships. Then, the hypergraph convolution is applied to model the high-order interactions between users and items. Additionally, we adopted a self-supervised learning task to maximize the consistency between different views. It helps to mitigate the model’s sensitivity to noisy feedback. We evaluated the performance of SHLKR through extensive experiments on publicly available datasets. The results demonstrate that leveraging hypergraphs for modeling can better capture the complexity and diversity of user preferences and interactions.