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Received:April 07, 2016 Revised:May 16, 2016
Received:April 07, 2016 Revised:May 16, 2016
中文摘要: 发现移动用户在特定时间段的轨迹特征是实现用户个性化推荐服务的关键之一.采用过滤——精炼策略,研究了如何从单用户的大量轨迹数据中发现其在较长时间内的特定时间段的兴趣点.在过滤阶段,将用户连续若干天中同一特定时间段内的轨迹数据进行基于密度的聚类,从而得到用户在这些天中每天的该特定时间段的停留点.在精炼阶段,对所有的停留点再一次聚类,进而得到用户在这些天中该特定时间段的兴趣点.最后,通过实验验证了该方法的有效性.
Abstract:Finding features of users' trajectories in a period of time is one of the key point to realize user's personalized recommendation service.In this paper, how to find the interests in a period from the large amount of user's trajectories is presented with a filter-refinement strategy.In the filter step, the user's trajectories in the same period for several certain days are clustered based on density to obtain the user's stops;in the refinement step, the stops are clustered to obtain the user's interests.Finally, experiments show the effectiveness of this work.
keywords: trajectories clustering stop and move interest
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基金项目:陕西省教育厅科学研究计划(14JK1307)；陕西省自然科学基金(2015JQ5157)；西安工程大学研究生创新基金(CX201630)
引用文本：
杨东山,张晓滨.基于过滤-精炼策略的用户特定时间段移动轨迹特征提取.计算机系统应用,2017,26(1):217-221
YANG Dong-Shan,ZHANG Xiao-Bin.Feature Extraction for Users' Trajectories in a Period Based on Filter-Refinement Strategy.COMPUTER SYSTEMS APPLICATIONS,2017,26(1):217-221
杨东山,张晓滨.基于过滤-精炼策略的用户特定时间段移动轨迹特征提取.计算机系统应用,2017,26(1):217-221
YANG Dong-Shan,ZHANG Xiao-Bin.Feature Extraction for Users' Trajectories in a Period Based on Filter-Refinement Strategy.COMPUTER SYSTEMS APPLICATIONS,2017,26(1):217-221
