Using check-in features to partition locations for individual users in location based social network
作者： Chen Yu , Baiyun Xiao , Dezhong Yao , Xiaofeng Ding , Hai Jin
DOI: 10.1016/J.INFFUS.2017.01.006
关键词: Computer science 、 Feature extraction 、 Information resource 、 Partition (database) 、 Data mining 、 Check-in 、 Majority rule
摘要: A concrete definition for the location classification problem is proposed.Features describing users check-in behaviors are used to partition locations.More side effects made by redundant features on non-linear models. With location-based social network (LBSN) flourishing, records offer us sufficient information resource do relative mining. Among locations visited a user, those attracting relatively more visits from that user can serve as support further mining and improvement services. Therefore, great significance lies in based visiting frequency. The aim of our paper individual utilizing machine learning, categorizing once he or she makes initial there. After feature extraction each record, we evaluate contribution three categories. results show different categories varies classification, where appear least contribution. At last, final test whole sample, comparing with two baselines majority voting respectively. largely outperform general, demonstrating effectiveness classification.
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