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，人们彼此共享与位置相关的信息，并利用从用户生成的和与位置相关的内容中学习到的协作知识。在各种LBSN服务中，位置推荐服务基于收集的时空信息向用户推荐未访问的站点，这是许多基于位置的服务的关键构建块，例如提高服务质量，客户参与和交通管理。本研究的长期目标是从异质移动数据中表征人类移动特性，以了解人类移动的潜在动力学，并为各种空间应用构建可靠的预测模型。申请补助金的主要目标是设计和开发一套基于LBSN和其他相关时空数据集上人类行为数据的位置预测和推荐方法。 本文的研究将为基于位置的服务和大时空数据挖掘研究做出重要贡献。本研究所产生的新的位置推荐模型将产生更智能、更准确的位置预测和推荐。这将有助于建立一个新的推荐模式，这是推荐“在正确的时间正确的位置”，并提供更好的理解与外部环境相关的用户行为之间的关系，用户的内部活动，反映在他们的行为LBSN。 该研究将有广泛的应用，包括基于用户位置偏好的本地广告推广产品和服务，基于用户意图的智能个人和汽车导航，社区活动的应急响应规划，旅行和旅游规划，车队管理，地理空间游戏和许多其他基于位置的服务应用。移动性预测为位置感知服务提供了吸引人的主动体验，并为企业和政府提供了必要的情报。 这项工作将为加拿大这一快速发展的地理空间技术的发展做出重大贡献。知识和软件的技术转让存在潜力，对加拿大有经济利益。 总之，该方案为位置推荐及其应用提供了新颖的思想、创新的方法和独特的解决方案。现有的研究设施、专门知识和研究经验为实现拟议目标和培养高素质人才提供了独特的机会。