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，人们相互共享位置相关信息，并利用从用户生成的位置相关内容中学习到的协作知识。在各种 LBSN 服务中，位置推荐服务向用户推荐未访问过的站点。收集的时空信息是许多基于位置的服务的关键组成部分，例如提高服务质量、客户参与度和交通管理。这项研究的长期目标是表征人类运动特性。来自异构移动数据为了了解人类流动的基本动态并为各种空间应用构建可靠的预测模型，所请求资助的主要目标是设计和开发一套基于 LBSN 和人类行为数据的位置预测和推荐方法。其他相关时空数据集。 所提出的研究将为基于位置的服务（LBS）和大时空数据挖掘研究做出原创性和重大贡献。该研究产生的新位置推荐模型将产生更加智能和准确的位置预测和推荐，这将有助于建立一个新的新兴位置。推荐范式，即“在正确的时间推荐正确的位置”，更好地理解用户与外部环境相关的行为与用户在LBSN上的行为所反映的内部活动之间的关系。 该研究将具有广泛的应用，包括根据用户位置偏好推广产品和服务的本地广告、根据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程序为位置感知服务提供有吸引力的主动体验，并为企业和政府提供重要的情报。这项工作将为这一快速发展的地理空间技术的发展做出重大贡献。加拿大存在潜力。知识和软件的技术转让，为加拿大带来经济利益。 总之，该提案为选址推荐及其应用提供了新颖的想法、创新的方法和独特的解决方案，现有的研究设施、专业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。