基于图神经网络的兴趣点推荐方法研究进展
方金凤,讲师,研究方向为时空大数据、兴趣点推荐、轨迹预测等,电子信箱:lnfangziyi@163.com |
收稿日期: 2024-01-05
网络出版日期: 2025-06-13
基金资助
辽宁省教育厅理工类项目(JYTQN2023211)
版权
Research progress on point-of-interest recommendation methods based on graph neural networks
Received date: 2024-01-05
Online published: 2025-06-13
Copyright
交叉学科是新科学的生长点,是科学发展的必然趋势。基于位置服务的重要应用——兴趣点推荐,作为计算机学科与地理信息学科相交叉的研究课题,对于推动2个学科在时空数据分析等相关领域的交叉融合研究具有重要作用。分析了兴趣点推荐的影响因素即地理位置、时间因素、社交关系和流行度,重点阐述了基于图神经网络的兴趣点推荐方法,包括基于图注意力网络、图卷积网络、图自编码器的推荐,并对其特点进行对比;讨论了在兴趣点推荐中存在的一些关键挑战,如数据稀疏性、冷启动问题和用户动态偏好问题,并针对各项挑战提出相应的解决思路,提出了结合多种影响因素的推荐,跨领域推荐以及动态偏好推荐的发展趋势。
方金凤 , 陈祖颐 . 基于图神经网络的兴趣点推荐方法研究进展[J]. 科技导报, 2025 , 43(9) : 76 -83 . DOI: 10.3981/j.issn.1000-7857.2024.01.00099
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