Hypergraph-Based Personalized Recommendation & Optimization Algorithm in EBSN

The service of personalized recommendations in event-based social networks (EBSN) is a very significant and valuable issue. Most of existing research work are mainly based on the ordinary graph to model relationships in EBSN. However, EBSN is a heterogeneous and complex network with many different types of entities. Because of that, modeling EBSN with ordinary graphs has the problem of high-dimensional information loss, resulting in reduced recommendation quality. Based on this background, in this paper, we first propose a hypergraph-based personalized recommendation (PRH) algorithm in EBSN. The basic idea is to make use of the characteristics of hypergraphs without losing high-dimensional data information to model high-dimensional complex social relationship data in EBSN more accurately, and to use regularized calculation of manifold ordering to obtain preliminary recommendation results. Next, this paper proposes an optimized PRH (oPRH) algorithm from the perspective of improving the query vector setting method and applying diverse weights to all sorts of different types of super edges to further optimize the recommendation results obtained by the PRH algorithm, so as to achieve accurate recommendation. The extended experiments show that the hypergraph-based personalized recommendation algorithm in EBSN and its optimization algorithm have higher accuracy than the previous ordinary graph-based recommendation algorithms.