|Abstract||本文针对目前海量的地理社会媒体数据与用户需求的信息之间存在“知识鸿沟”问题，面向地理社 会媒体的挖掘与应用开展深入研究。特别地，把语义理解、知识挖掘与应用服务结合起来，贯穿到研究的 问题中，具体做了三个方面的研究工作。① 地理位置计算。以地理位置为中心，旨在利用带地理标签的媒 体数据，结合地理位置信息和多媒体语义，进行地理位置的建模和知识挖掘。分别提出了一种基于区域隐 式支持向量机模型框架和系统性的可视化方法框架和概率图模型。② 用户理解。以用户为中心，利用用户 产生的大量的媒体内容数据和丰富的用户网络行为数据进行用户分析建模。分别提出了一种关联性隐式支 持向量机模型框架和基于超图学习的方法框架。③ 用户与地理位置结合的建模分析。从地理社会媒体数据 挖掘得到的地理位置和用户的知识，需要通过有价值的应用送达给终端用户，满足用户的需求。针对用户 在移动场景下的地理位置评分行为的特性，提出了一种场景感知回归混合模型对时空场景信息、用户兴趣、 地理区域偏好、地理物品与内容进行统一建模并实现推荐。分别在真实数据集上测试，验证了所提方法框 架的有效性，并进行了多种面向用户的应用，证明了地理社会媒体挖掘的实用性。|
With the development of information technology and the Internet, especially the proliferation of mobile Internet, social media is a new tool and platform that allows people to create and share information. Its rapid development has attracted attentions from hundreds of millions worldwide users. Meanwhile, along with the advancement of geographic positioning technology, especially the popularity of smart mobile phones, location-based services have become mainstream applications. In this context, the hybrid of geo-location and social media forms georeferenced social media, which includes various types of social media services featured with geographical locations. Georeferenced social media that enables users to access and share information “anytime, anywhere“, has generated a huge amount of geo-tagged social media data, which is heterogeneous, multimodal, and spatio-temporal. There is a “knowledge gap” between the massive georeferenced social media data and user information needs. Therefore, how to eﬀectively and eﬃciently conduct data mining to harvest knowledge for end-user services, becomes the key problem to the development of modern Internet.
Georeferenced social media contains geo-locations, users, and data, which interrelate with each other. On the one hand, users have generated massive geographical media data, which can be aggregated to mine knowledge for understanding geographical locations. On the other hand, users have contributed rich online social behavior data, which can be explored to understand users. In this thesis, we investigate the research on georeferenced social media mining and application, which aims to explore the user-sensed georeferenced social media data to harvest knowledge for understanding geo-locations and users, thus providing valuable applications and services. In particular, we have combined semantic understanding, knowledge mining and application services in a principled and uniﬁed framework to conduct the research work concerning the following three issues: • Geo-location computing. User-generated geographical data contains geograph
ic related knowledge and information of our world and human society. Geolocation computing aims to exploit the geo-tagged media content for modeling geo-locations and discovering geographic knowledge by combining geographical information and multimedia semantics in a uniﬁed way. The problems of geo-location computing include geographical location estimation, location theme discovery, multimodal aspect-opinion mining, and sentiment analysis. The harvested geographic knowledge can enable potential applications such as location recognition, visualization, and exploration. • User understanding. To truly bridge the gap between data and user information requirements, besides multimedia semantic analysis, another key issue is user interest and intent understanding. This research line attempts to explore rich user-contributed multimedia content and social behaviors to model users for harvesting user proﬁles and behavior patterns. The detailed research problems include user attribute inference and inﬂuence modeling. • Jointly modeling users and geo-locations. The knowledge discovered from georeferenced social media data should be integrated with appropriate application mechanisms to satisfy users’ needs. This research direction combines users, geo-locations, and data in a uniﬁed framework to mine knowledge of users and geo-locations, which is further incorporated into an established recommendation system to support location exploration.
|Keyword||：社会媒体 用户理解 知识挖掘 可视化 识别预测 探索发现 个性化服务|
|方全. 面向地理社会媒体的挖掘与应用[J]. 中国人工智能学会通讯,2016,6(11):6.|
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