Since people with close relationships are easily influenced by each other, social friends usually have more preferences of higher similarities than others. For this reason, social recommendation methods are proposed to adopt social links to improve the degree of preference matching between users and recommended items. Although current social recommendation methods have captured the general preference similarities among social friends, it is still difficult to model the evolution of dynamic social influence among friends, especially in session-based scenarios. In reality, when users’ dynamic preferences are changing, social feedback from their friends is also changing over time. So that the dynamic social feedback is an important social influence, which has not been considered in current studies. To this end, we propose a social feedback-enhanced session-based recommendation (SFRec) method, which not only utilizes the similarity of general preferences among friends but also captures the friends’ influence which reflects people’s dynamic preferences. Specifically, we first coordinate similarity relations via information propagation on social graph, item transition graph and user–item interaction graph. To capture social feedback based on users’ dynamic preferences, we then construct a social feedback generation module that consists of preference extraction, feedback generation and feedback aggregation. Finally, we construct a preference fusion module to obtain the final preference representation and make personalized recommendation. We conduct comprehensive experiments on three datasets. Results demonstrate that SFRec surpasses the state-of-the-art models on recommendation performance.

Successful applications of the gene ontology to infer functional relationships between gene products in recent years have raised the need for computational methods to automatically calculate semantic similarity between gene products based on the gene ontology. To meet this challenge, several methods have been proposed to derive semantic similarity between gene products based on semantic similarity of gene ontology terms. However, these methods, though having been widely used in a variety of applications, may significantly overestimate semantic similarity between genes that are actually not functional related, thereby yielding misleading results in applications. To overcome this limitation, we propose to represent a gene product as a vector that is composed of information contents of gene ontology terms annotated for the gene product. Results show that semantic similarity scores calculated using our proposed method are more consistent with known biological knowledge than those derived using a list of existing methods, suggesting the effectiveness of our method in characterizing functional relationships between gene products.