Personalized Location Recommendation on Location-Based Social Networks by Efficiently Utilizing Spatio-Temporal Information

有效利用时空信息的基于位置的社交网络的个性化位置推荐

• 批准号: RGPIN-2018-03916
• 负责人: Wang, Xin
• 金额: $ 3.13万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711374
• 关键词: Personalized; Location; Recommendation; Based; Social

In location-based social networks (LBSN), people share location-related information with each other and leverage collaborative knowledge learned from user-generated and location-related content. Among various LBSN services, the location recommendation service suggests unvisited sites to the users based on the spatio-temporal information collected, which is a critical building block in many location-based services, such as the improvement of service quality, customer engagement and transportation management. The long-term objective of this research is the characterization of human movement properties from heterogeneous mobility data in order to understand the underlying dynamics of human mobility and to construct reliable predictive models for various spatial applications. The primary goal for the requested grant is the design and development of a set of locational prediction and recommendation methods based on human behaviour data on LBSNs and other relevant spatiotemporal datasets. The proposed research will make original and significant contributions in location-based services (LBS) and big spatiotemporal data mining research. New location recommendation models produced from this research will generate more intelligent and accurate location predication and recommendation. It will help built a new emerging paradigm of recommendation, which is to recommend “the right location at the right time” and provide the better understanding to the relationships between users' behavior related to external environmental, and users' internal activities that be reflected in their behaviors on LBSN. The research will have a wide range of applications, including local advertisement to promote the products and services based on users' locational preferences, intelligent personal and car navigation based on the user's intent, emergency response planning for community events, travel and tour planning, fleet management, geospatial gaming and many other location-based service applications. Mobility prediction enables appealing proactive experiences for location-aware services and offers essential intelligence to businesses and governments. This work will make a significant contribution to the development of this rapidly growing geospatial technology in Canada. Potential exists for the technology transfer of knowledge and software, with economic benefit to Canada. In summary, the proposal offers novel ideas, innovative approaches and distinctive solutions to location recommendation and its applications. The available research facilities, expertise and research experience provides unique opportunities to achieve the proposed objectives and train highly qualified personnel.

在基于位置的社交网络（LBSN）中，人们相互共享与位置相关的信息，并利用从用户生成的和位置相关的内容中学到的协作知识。在各种LBSN服务中，位置建议服务根据收集的时空信息向用户提供未访问的站点，这是许多基于位置的服务的关键构建块，例如改善服务质量，客户参与和运输管理。这项研究的长期目标是从异质迁移数据中表征人类运动特性，以了解人类流动性的潜在动态，并为各种空间应用构建可靠的预测模型。请求赠款的主要目标是基于人类行为数据的LBSN和其他相关空间时间数据集的一组位置预测和建议方法的设计和开发。 拟议的研究将在基于位置的服务（LB）和大型时空数据挖掘研究中做出原始和重大贡献。这项研究产生的新位置建议模型将产生更聪明，准确的位置预测和建议。它将有助于建立一个新的新兴推荐范式，即推荐“正确的时间在正确的时间”，并为用户与外部环境相关的用户行为与用户的内部活动之间的关系提供更好的理解，这些行为会反映在其lbsn上的行为。 这项研究将具有广泛的应用程序，包括本地广告，以根据用户的位置偏好，智能的个人和汽车导航来推广产品和服务，这是根据用户意图，社区活动，旅行和旅游计划，车队管理，地理空间游戏以及许多其他基于位置的服务应用程序的智能个人和汽车导航。流动性预测使现场感知服务的积极主动体验，并为企业和政府提供基本的情报。这项工作将为加拿大这种快速发展的地理空间技术的发展做出重大贡献。知识和软件的技术传输存在潜在的潜力，并为加拿大带来了经济利益。 总而言之，该提案为位置建议及其应用提供了新颖的想法，创新方法和独特的解决方案。可用的研究设施，专业知识和研究经验为实现拟议目标和培训高素质的人员提供了独特的机会。