Point-of-Interest recommendation algorithm combining location influence

doi:10.11772/j.issn.1001-9081.2019051087

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (11): 3178-3183.DOI: 10.11772/j.issn.1001-9081.2019051087

• The 2019 CCF Conference on Artificial Intelligence (CCFAI2019) • Previous Articles Next Articles

Point-of-Interest recommendation algorithm combining location influence

XU Chao¹, MENG Fanrong¹, YUAN Guan¹, LI Yuee², LIU Xiao¹

1. College of Computer Science and Technology, China University of Mining and Technology, Xuzhou Jiangsu 221116, China;
2. Archives, China University of Mining and Technology, Xuzhou Jiangsu 221116, China

Received:2019-05-24 Revised:2019-06-30 Online:2019-09-11 Published:2019-11-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (71774159), the China Postdoctoral Fund Project (2018M642358), the Think Tank of Green Safety Management and Policy Science (2018WHCC03).

融合地点影响力的兴趣点推荐算法

许朝¹, 孟凡荣¹, 袁冠¹, 李月娥², 刘肖¹

1. 中国矿业大学计算机科学与技术学院, 江苏徐州 221116;
2. 中国矿业大学档案馆, 江苏徐州 221116

通讯作者: 袁冠
作者简介:许朝(1996-),男,江苏徐州人,硕士研究生,主要研究方向:基于位置的社交网络计算;孟凡荣(1962-),女,辽宁沈阳人,教授,博士,CCF高级会员,主要研究方向:数据库、数据挖掘;袁冠(1982-),男,江苏徐州人,副教授,博士,CCF高级会员,主要研究方向:时空数据挖掘;李月娥(1983-),女,江苏淮安人,硕士,主要研究方向:信息管理系统;刘肖(1994-),江苏徐州人,硕士研究生,主要研究方向:模式识别。
基金资助:
国家自然科学基金资助项目（71774159）；中国博士后基金资助项目（2018M642358）；绿色安全管理与政策科学智库（2018WHCC03）。

Abstract

Abstract: Focused on the issue that Point-Of-Interest (POI) recommendation has low recommendation accuracy and efficiency, with deep analysis of the influence of social factors and geographical factors in POI recommendation, a POI recommendation algorithm combining location influence was presented. Firstly, in order to solve the sparseness of sign-in data, the 2-degree friends were introduced into the collaborative filtering algorithm to construct a social influence model, and the social influence of the 2-degree friends on the users were obtained by calculating experience and friend similarity. Secondly, by deep consideration of the influence of geographical factors on POI, a location influence model was constructed based on the analysis of social networks. The users' influences were discovered through the PageRank algorithm, and the location influences were calculated by the POI sign-in frequency, obtaining overall geographical preference. Moreover, kernel density estimation method was used to model the users' sign-in behaviors and obtain the personalized geographical features. Finally, the social model and the geographic model were combined to improve the recommendation accuracy, and the recommendation efficiency was improved by constructing the candidate POI recommendation set. Experiments on Gowalla and Yelp sign-in datasets show that the proposed algorithm can quickly recommend POIs for users, and has high accuracy and recall rate than Location Recommendation with Temporal effects (LRT) algorithm and iGSLR (Personalized Geo-Social Location Recommendation) algorithm.

Key words: point-of-interest recommendation, location-based social network, collaborative filtering algorithm, location influence, kernel density estimation

摘要： 为解决兴趣点（POI）推荐不准确和效率低的问题，深入分析社交因素和地理位置因素的影响，提出了一种融合地点影响力的POI推荐算法。首先，为了解决签到数据稀疏的问题，将2-度好友引入协同过滤算法中构建了社交影响模型，通过计算经历和好友相似度获取2-度好友对用户的社交影响；其次，深入考虑地理位置因素对POI推荐影响，在对社交网络分析的基础上构造了地点影响力模型，通过PageRank算法发现用户影响力，结合POI被签到次数计算地点影响力，获取准确的整体位置偏好，并使用核密度估计方法对用户签到行为建模和获取个性化地理位置特征；最后，融合社交模型和地理位置模型提高推荐准确性，并通过构造POI推荐候选集来提高推荐效率。在Gowalla和Yelp签到数据集上实验，结果表明所提算法能够快速完成POI推荐，在准确率和召回率指标上明显优于融合时间因素的位置推荐（LRT）和融合地理社交因素的个性化位置推荐（iGSLR）算法。

CLC Number:

TP391.1

XU Chao, MENG Fanrong, YUAN Guan, LI Yuee, LIU Xiao. Point-of-Interest recommendation algorithm combining location influence[J]. Journal of Computer Applications, 2019, 39(11): 3178-3183.

许朝, 孟凡荣, 袁冠, 李月娥, 刘肖. 融合地点影响力的兴趣点推荐算法[J]. 计算机应用, 2019, 39(11): 3178-3183.

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Point-of-Interest recommendation algorithm combining location influence

融合地点影响力的兴趣点推荐算法

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