Hypergraph-Based Personalized Recommendation & Optimization Algorithm in EBSN

被引：0
|
作者：
Yu Y. [1 ,2 ]
Zhang W. [1 ,2 ]
Li Z. [1 ,2 ]
Li Y. [1 ,2 ]
机构：
[1] School of Computer Science and Engineering, Northeastern University, Shenyang
[2] Key Laboratory of Intelligent Computing in Medical Image, Northeastern University, Ministry of Education, Shenyang
来源：
Jisuanji Yanjiu yu Fazhan/Computer Research and Development | 2020年 / 57卷 / 12期
基金：
中国国家自然科学基金;
关键词：
Accurate personalized recommendation; Event-based social networks (EBSN); Hypergraph; Manifold ranking; Optimization; Regularization computation;
D O I：
10.7544/issn1000-1239.2020.20190275
中图分类号：
学科分类号：
摘要：
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. © 2020, Science Press. All right reserved.
引用
收藏
页码：2556 / 2570
页数：14
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