Recently, online social networks have enriched the users’ lives greatly and social recommendation systems make it easier for users to discover more information that they are interested in. The most advanced graph neural network based social recommendation methods start to utilize the higher-order social relations, e.g. the friends of friends, to reveal users’ preferences. However, existing high-order methods ignore the implicit social relations among users and the users’ interests changing dynamically over time. In this paper, we propose a Multi-Order Hypergraph Convolutional Neural Network (MOHCN) for dynamic social recommendation system to improve the recommendation task, which models the users’ dynamic interest evolution at the session level. To compensate for the lack of social information of some users, we combine the implicit social relations obtained from user-item interaction graph with the explicit social relations from user-user social graph through hypergraph modeling. Extensive experimental results on three real-world datasets demonstrate the effectiveness of our proposed MOHCN compared with the state-of-the-art methods.

Understanding the relationship between the chemical structure and physicochemical properties of odor molecules and olfactory perception, i.e., the structure–odor relationship, remains a decades-old, challenging task. However, the differences among the molecular structure graphs of different molecules are subtle and complex, and the molecular feature descriptors are numerous, with complex interactions that cause multiple odor perceptions. In this paper, we propose to decompose the features of the molecular structure graph into feature vectors corresponding to each odor perception descriptor to effectively explore higher-order semantic interactions between odor molecules and odor perception descriptors. We propose an olfactory perception prediction model noted as HGAFMN, which utilizes a hypergraph neural network with the olfactory lateral inhibition-inspired attention mechanism to learn the molecular structure feature from the odor molecular structure graph. Furthermore, existing methods cannot effectively extract interactive features in the large number of molecular feature descriptors, which have complex relations. To solve this problem, we add an attentional factorization mechanism to the deep neural network module and obtain a molecular descriptive feature through the deep feature combination based on the attention mechanism. Our proposed HGAFMN has achieved good results in extensive experiments and will help product design and quality assessment in the food, beverage, and fragrance industries.