A social friend selection method based on interest and social behavior

Social recommendation algorithms that integrate social information have become a research hotspot. However, the existing social recommendation has the problem that it is difficult to obtain reliable and clear friend relationships. Therefore, this paper proposes a social friend selection method FSISB based on interests and social behaviors. This method first defines four types of scenarios to find the explicit/hidden social friends of the target user, and alleviates the sparsity problem of social data by expanding the social scope; then by combining the user’s social structure and long-term/short-term interests to calculate the similarity of social friends, gets reliable and unambiguous social information and complete friend selection. Experiments show that the baseline model integrated with the FSISB method improves the recommendation performance by 0.4%-1.5% compared with the original method, and the performance improvement is more obvious in datasets with sparse social data, which verifies the positive impact of the FSISB method on alleviating data sparseness.