ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2019, Vol. 56 ›› Issue (11): 2506-2517.doi: 10.7544/issn1000-1239.2019.20180673

• 信息处理 • 上一篇    



  1. 1(浙江工业大学计算机科学与技术学院 杭州 310023);2(浙江理工大学信息学院 杭州 310018);3(华中科技大学软件学院 武汉 430074) (
  • 出版日期: 2019-11-12
  • 基金资助: 

Collaborative Recommendation Method Based on Community Co-Clustering in Location Based Social Networks

Gong Weihua1, Jin Rong2, Pei Xiaobing3, Mei Jianping1   

  1. 1(School of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023);2(School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018);3(Software Institute, Huazhong University of Science and Technology, Wuhan 430074)
  • Online: 2019-11-12

摘要: 近年来,异质网络中的社区发现逐渐成为人们关注的研究热点,然而现有大多数非重叠或重叠的社区发现方法都局限于考虑单一类型的网络结构,而无法适用于包含多模实体及其多维关系的异质网络,基于位置的社交网络(location based social network, LBSN)作为最近兴起的一种新型异质网络,如何有效发现其含有多维关系的复杂社区结构对现有研究来说是一个挑战性的难题.为此,提出了一种融合用户与位置实体及其多维关系的社区发现方法MRNMF(multi-relational nonnegative matrix factorization),该方法通过建立基于非负矩阵分解的联合聚类目标函数,并考虑融入用户社交关系、用户-位置签到关系以及兴趣点特征等多维度的影响因素,能同时获得紧密关联的用户模糊社区与兴趣点聚簇结构,以有效缓解推荐中的数据稀疏问题.在2种真实LBSN数据集上的实验结果表明,所提出的MRNMF方法同时在兴趣点与朋友这双重推荐上比其他传统方法具有更优越的推荐性能.

关键词: 基于位置的社交网络, 联合聚类, 重叠社区, 非负矩阵分解, 兴趣点推荐

Abstract: In recent years, community discovery in heterogeneous networks has gradually become a research hotspot. However, most of the existing methods for discovering non-overlapping or overlapping communities only take one single type of information network into account, and cannot be applied to heterogeneous networks containing multi-mode entities and their multi-dimensional relationships. Presently as a new emerging heterogeneous network, location based social network (LBSN) is attracting more and more attention from social network field. How to effectively discover the hidden complex community structures with multi-dimensional relationships in LBSN, is a very challenging problem for current researchers. Therefore, a community discovery method called multi-relational nonnegative matrix factorization (MRNMF) is proposed that integrates both user and location entities and fuse their multidimensional relationships in LBSN. This method establishes a joint clustering objective function based on nonnegative matrix factorization (NMF), and considers the effect of multi-dimensional factors such as user social relations, user-location check-ins and features of points of interests (POIs). The merits are that not only obtaining accurate user fuzzy communities, but also getting closely related clusters of POIs, which can effectively alleviate data sparse problem in recommendations. The experimental results on two real LBSN datasets show that the proposed method MRNMF has better recommendation performance than other traditional methods in the dual recommendations for POIs and users.

Key words: location based social network (LBSN), co-clustering, overlapping community, nonnegative matrix factorization (NMF), point of interests recommendation