摘要:
基于事件的社交网(event-based social networks, EBSN)中的个性化推荐服务是一个十分重要且颇具应用价值的问题,现有研究工作主要基于普通图来对EBSN中的关系进行建模,但由于EBSN是一种异构型复杂社交网络,具有多种不同类型实体,因而用普通图建模EBSN会存在高维信息丢失问题,导致推荐质量降低.基于此,首先提出一种基于超图模型的EBSN个性化推荐(hypergraph-based personalized recommendation in EBSN, PRH)算法,其基本思想在于利用超图具有不丢失高维数据信息之特点来更准确地对EBSN中复杂社交关系数据进行高维建模,并利用流形排序正则化计算获取初步推荐结果.其次,又分别从查询向量设置方式改进和对不同类超边施以不同权重等角度,提出了优化的PRH(optimized PRH, oPRH)算法以进一步优化PRH算法所获推荐结果,从而实现精准推荐.扩展实验表明,基于超图的EBSN个性化推荐及其优化算法,推荐结果相比于以前基于普通图的推荐算法具有更高准确性.
Abstract:
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.
百度学术
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