Measuring user similarity using check-ins from LBSN: a mobile recommendation approach for e-commerce and security services
Measuring user similarity using check-ins from LBSN: a mobile recommendation approach for e-commerce and security services
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DOI：
10.1080/17517575.2019.1686655
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发表时间：
2019-11
影响因子：
4.4
通讯作者：
Haidong Zhong;Hongbo Lyu;Shaozhong Zhang;P. Li;Z. Zhang;Li D. Xu
中科院分区：
文献类型：
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作者：
Haidong Zhong;Hongbo Lyu;Shaozhong Zhang;P. Li;Z. Zhang;Li D. Xu
摘要社会友谊和地理位置信息往往反映了个人的个人偏好和其他类型的知识，可以用来提取他们的相似性推荐系统。本文发现，用户更有可能在一些特定的中心移动或在一些热点地区签到;少数人经常签到，而大多数地点很少被访问。基于这些发现，我们提出了一个多中心聚类算法来捕获用户的移动的模式，并开发了一个用户相似性度量方法。复杂度分析表明该方法在处理大数据集时是有效的，实验结果表明该方法具有良好的适用性。
ABSTRACT Social friendship and geographical position information often reflects individuals’ personal preferences and other types of knowledge that can be used to extract their similarity for recommendation systems. This paper finds that users are more likely to move around some specific centres or check in at some hotspots; a few individuals check in frequently, whereas most locations are rarely visited. Based on these findings, we propose a multi-centre clustering algorithm to capture users’ mobile patterns and develop a user similarity measurement method. Complexity analysis shows the method’s efficiency in handling large datasets and experimental results demonstrate its good applicability.