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Personalized Travel Route Recommendation Using Collaborative
时间:2017-06-07 点击:
主题:Personalized Travel Route Recommendation Using Collaborative Filtering Based on GPS Trajectories
时间:
612日下午15
地点:主楼四区107
王欣教授简历

陕西省百人计划学者,加拿大卡尔加里大学终身教授、博士生导师,GIS学科带头人,加拿大人工智能委员会执行委员,ACM委员会委员,全球CPGIS委员会委员,第1届(香港),第2届(韩国斧山)空间信息管理与数据挖掘国际合作工作组主席, 第12届国际Web及无线GIS国际会议主席, 第17届澳大利亚知识发现与数据挖掘亚太国际会议副主席, 第三届和第四届IEEE 数据科学和分析国际会议组织委员会委员。长期从事人工智能、数据挖掘等方面的科学研究与教学工作,发表学术论文80余篇,获得加拿大国家科学研究奖励1项,获加拿大阿尔伯特省科研奖励1项,多次在各种国际会议上做主题报告,主持加拿大自然科学与工程技术等研究项目25项, 参与中国国家自然科学基金2项和陕西省国际科技合作与交流计划项目共4项。 王教授先后与复旦大学武汉大学、武汉理工大学、西安交通大学、天津大学、华东理工大学、陕西师范大学等进行科学研究与教学合作,并做学术报告。


摘要:
Travelling is a critical component of daily life. With new technology, personalized travel route recommendations are possible and have become a new research area. A personalized travel route recommendation refers to plan an optimal travel route between two geographical locations, based on the road networks and users’ travel preferences. In this talk, I will define users’ travel behaviours from their historical GPS trajectories and propose two personalized travel route recommendation methods­ – CTRR and CTRR+. Both methods consider users’ personal travel preferences based on their historical GPS trajectories. In this paper, we first estimate users’ travel behaviour frequencies by using collaborative filtering technique.  A route with the maximum probability of a user’s travel behaviour is then generated based on the naïve Bayes model. The CTRR+ method improves the performances of CTRR by taking into account cold start users and integrating distance with the user travel behaviour probability. This paper also conducts some case studies based on a real GPS trajectory dataset from Beijng, China. The experimental results show that the proposed CTRR and CTRR+ methods achieve better results for travel route recommendations compared with the shortest distance path method.