融入时间的兴趣点协同推荐算法

doi:10.11772/j.issn.1001-9081.2020101565

计算机应用 ›› 2021, Vol. 41 ›› Issue (8): 2406-2411.DOI: 10.11772/j.issn.1001-9081.2020101565

所属专题：第八届CCF大数据学术会议（CCF Bigdata 2020）

• 第八届CCF大数据学术会议 • 上一篇下一篇

融入时间的兴趣点协同推荐算法

包玄, 陈红梅, 肖清

云南大学信息学院, 昆明 650500

收稿日期:2020-10-12 修回日期:2020-12-14 出版日期:2021-08-10 发布日期:2021-01-27
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:
国家自然科学基金资助项目（61662086，61966036）；云南省创新团队项目（2018HC019）。

Time-incorporated point-of-interest collaborative recommendation algorithm

BAO Xuan, CHEN Hongmei, XIAO Qing

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).

摘要/Abstract

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

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

中图分类号:

TP391.3

包玄, 陈红梅, 肖清. 融入时间的兴趣点协同推荐算法[J]. 计算机应用, 2021, 41(8): 2406-2411.

BAO Xuan, CHEN Hongmei, XIAO Qing. Time-incorporated point-of-interest collaborative recommendation algorithm[J]. Journal of Computer Applications, 2021, 41(8): 2406-2411.

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融入时间的兴趣点协同推荐算法

Time-incorporated point-of-interest collaborative recommendation algorithm

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