Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (8): 2406-2411.DOI: 10.11772/j.issn.1001-9081.2020101565

Special Issue: 第八届CCF大数据学术会议(CCF Bigdata 2020)

• CCF Bigdata 2020 • Previous Articles     Next Articles

Time-incorporated point-of-interest collaborative recommendation algorithm

BAO Xuan, CHEN Hongmei, XIAO Qing   

  1. School of Information Science and Engineering, Yunnan University, Kunming Yunnan 650500, China
  • Received:2020-10-12 Revised:2020-12-14 Online:2021-08-10 Published:2021-01-27
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61662086, 61966036), the Project of Innovative Research Team of Yunnan Province (2018HC019).


包玄, 陈红梅, 肖清   

  1. 云南大学 信息学院, 昆明 650500
  • 通讯作者: 陈红梅
  • 作者简介:包玄(1995-),女,云南曲靖人,硕士研究生,CCF会员,主要研究方向:空间数据挖掘;陈红梅(1976-),女,重庆人,副教授,博士,CCF会员,主要研究方向:数据库、空间数据挖掘;肖清(1975-),女,江西吉水人,讲师,硕士,CCF会员,主要研究方向:空间数据挖掘。
  • 基金资助:

Abstract: Point-Of-Interest (POI) recommendation aims to recommend places that users do not visit but may be interested in, which is one of the important location-based services. In POI recommendation, time is an important factor, but it is not well considered in the existing POI recommendation models. Therefore, the Time-incorporated User-based Collaborative Filtering POI recommendation (TUCF) algorithm was proposed to improve the performance of POI recommendation by considering time factor. Firstly, the users' check-in data of Location-Based Social Network (LBSN) was analyzed to explore the time relationship of users' check-ins. Then, the time relationship was used to smooth the users' check-in data, so as to incorporate time factor and alleviate data sparsity. Finally, according to the user-based collaborative filtering method, different POIs were recommended to the users at different times. Experimental results on real check-in datasets showed that compared with the User-based collaborative filtering (U) algorithm, TUCF algorithm had the precision and recall increased by 63% and 69% respectively, compared with the U with Temporal preference with smoothing Enhancement (UTE) algorithm, TUCF algorithm had the precision and recall increased by 8% and 12% respectively. And TUCF algorithms reduced the Mean Absolute Error (MAE) by 1.4% and 0.5% respectively, compared with U and UTE algorithms.

Key words: location-based service, Point-Of-Interest (POI), recommendation, collaborative filtering, time relationship

摘要: 兴趣点(POI)推荐可以帮助用户发现其没有访问过但可能感兴趣的地点,是重要的基于位置的服务之一。时间在POI推荐中是一个重要因素,而现有POI推荐模型并没有较好地考虑时间因素,因此通过考虑时间因素来提出融入时间的POI协同推荐(TUCF)算法,从而提高POI推荐的效果。首先,分析基于位置的社交网络(LBSN)的用户签到数据,以探索用户签到的时间关系;然后,利用时间关系对用户签到数据进行平滑处理,以融入时间因素并缓解数据稀疏性;最后,根据基于用户的协同过滤方法,在不同时间推荐不同POI给用户。在真实签到数据集上的实验结果表明,与基于用户的协同过滤(U)算法相比,TUCF算法的精确率和召回率分别提高了63%和69%;与具有平滑增强时间偏好的协同过滤(UTE)算法相比,TUCF算法的精确率和召回率分别提高了8%和12%;并且TUCF算法的平均绝对误差(MAE)比U算法和UTE算法分别减小了1.4%和0.5%。

关键词: 基于位置的服务, 兴趣点, 推荐, 协同过滤, 时间关系

CLC Number: