融入空间关系的矩阵分解POI推荐模型  

Integrating Spatial Relationship into a Matrix Factorization Model for POI Recommendation

作　　者：魏海涛[1,2] 李柯[1,2] 赫晓慧[1,2] 田智慧[1,2] WEI Haitao;LI Ke;HE Xiaohui;TIAN Zhihui(School of Geo-Science and Technology,Zhengzhou University,Zhengzhou 450052,China;Joint Laboratory of Eco-Meteorology,Zhengzhou University,Chinese Academy of Meteorological Sciences,Zhengzhou 450052,China)

机构地区：[1]郑州大学地球科学与技术学院,河南郑州450052 [2]郑州大学中国气象科学研究院生态气象联合实验室,河南郑州450052

出　　处：《武汉大学学报（信息科学版）》2021年第5期681-690,共10页Geomatics and Information Science of Wuhan University

基　　金：国家重点研发计划(2018YFB0505000);河南省重点研发与推广专项(科技攻关)(192102210124)。

摘　　要：兴趣点(point of interest, POI)推荐是在基于位置的社交网络中流行起来的个性化服务。针对数据稀疏和隐性反馈的使用等问题,提出了一种关系型矩阵分解模型——合作竞争矩阵分解(cooperative competition matrix factorization,CC-MF)。该模型根据用户与POI间的相互关系建模,融入空间关系,并将空间关系细分为空间距离关系和空间拓扑关系,挖掘POI之间、POI与用户之间的空间关系,以缓解数据稀疏问题;同时使用加权最小二乘准则构建目标函数,缓解隐性反馈问题。在现实世界签到Foursquare数据集上进行实验,结果显示:(1)CC-MF模型显著提高了推荐结果的准确性;(2)考虑空间拓扑关系的空间距离因素能够进一步提升推荐系统的性能。因此,CC-MF模型具有良好的拓展性和解释性,且缓解了数据稀疏和隐性反馈使用问题。Objectives: Point of interest(POI) recommendation is the prevalent personal service in location-based social network(LBSN), and aims to provide personalized recommendation services by using the information carried by LBSN. The utilization of spatial relationship information as the side information supplies a chance to product better POI recommend. However, thousands of users and POIs in the LBSN make the user-POI check-in matrix very large and sparse.In addition, check-in record data is typical implicit feedback data, which cannot directly reflect the user.s preference. To tackle the aforementioned challenges,we propose a relational matrix factorization model based on cooperative competition matrix factorization(CC-MF). Methods: The CC-MF model can simulate the relationship between users and POIs, and divides spatial relationships into spatial distance relationship and spatial topological relationship. In order to alleviate the problem of data sparsity, the model excavates the spatial relationships among POIs, POIs and users by integrating spatial relationships. Firstly, we use nonlinear function to establish the spatial distance relationship between users and POIs, which can connect the relationship between users and POIs. Then,k-nearest neighbor(k NN) algorithm is used to calculate the geo-neighbors of POI by considering the spatial distance factor of spatial topological relationship, which can further alleviate the sparsity of data. Finally,the spatial relationship is integrated into the matrix factorization model. Meanwhile, the weighted least square method is used as the objective function of the CC-MF model to relieve the implicit feedback problem. Experiments are carried out on the real-world check-in Foursquare datasets. We test the recommendation performance of the proposed model and baseline methods, and analyze the crucial influence of different spatial relationships on POI recommendation. The precision and recall are used as evaluation metrics.Results: The results show that:(1) The CC-MF model signifi

关 键 词：兴趣点推荐 基于位置的社交网络 矩阵分解 空间关系 空间距离 

分 类 号：P208[天文地球—地图制图学与地理信息工程]