Geo-referenced Social Media Mining And Its Application

In recent years, the Internet has come into a mobile era, people are now used to browsing and sharing information through applications installed on their mobile devices. Most of these mobile applications are location based services, and users have generated a huge amount of geo-referenced social media while they’re using these location based services. The ever growing volume of geo-referenced social media has shed light upon many research fields, bringing challenges and opportunities to researchers.In contrast to conventional multimedia, the geo-referenced social media have unique characteristics, which lie in three aspects:Firstly, the geo-information and the way it organizes with media content is heterogeneous. Secondly, it emphasizes mobility, efficiency and user interaction. Thirdly, it contains various contextual information including spatial-temporal information, social information and multi-modality media. This dissertation focuses on the research problems brought by above three characteristics, and carries out the study in the following directions: heterogeneous structured data mining algorithm, efficient online location recommendation algorithm for mobile application and personalized location recommendation algorithm which exploits multiple types of contextual information.The content and contributions of this dissertation are as follows:1. Proposed a heterogeneous structured social media mining algorithm and a bipartite graph based ranking algorithm.In order to enrich location semantics in local review website and meet specific information need of travelers, the proposed algorithm combines both structured and unstructured geo-referenced social media to mine local semantics. After that, the proposed method applies a bipartite graph based ranking algorithm to re-rank the POIs in local review website. Experiments show that the algorithm can achieve a73%improvement in MAP compared to method that only uses structured data. Experiments also show that the bipartite graph based ranking algorithm can improve POIs original ranking in local review website so that it can fit travelers’information need.2. Proposed an efficient online recommendation algorithm for mobile applications.The online mobile recommendation problem requires time efficiency and dynamic model adjustment. To meet these requirements, the proposed algorithm extracts frequent sequential patterns from users’traveling history and uses these patterns to build prefix tree. Experiments show that in contrast to existing VMM algorithm probabilistic suffix tree (PST), the proposed algorithm reduces the time complexity from O(Dl) to O(l). Not only efficient, the proposed algorithm can also achieves better recommendation precision while combining with certain smoothing model:69%improvement compared to fixed order Markov model and36%improvement compared to PST. Because the proposed algorithm only relies on the user’s current location, it can be easily embedded into existing commercial location based service applications. Besides, if we extend the concept of’location’, the proposed algorithm can be applied in many other problems, such as user click prediction on web pages, query term recommendation in search engine, etc.3. Proposed a personalized recommendation algorithm which exploits various types of contextual information.The algorithm first exploits various contextual information from photo sharing websites including GPS coordinates, photos’taken time, user information, textual tags and photos’visual information. After that, the algorithm calculates user’s preference to a certain location from different aspects. Finally, the algorithm formulate the recommendation problem as a learning to rank problem, so that it combines preference predictions of different aspects to generate the final recommendation result. Experiments show the proposed algorithm improves recommendation precision, especially when the user’s traveling history contains little locations.42.7%users in experiment data set only have4locations in their travel history, in this situation the proposed algorithm achieves27.5%improvement compared to existing method. That is meaningful to alleviate the cold start problem. Besides, the recommendation algorithm based on learning to rank can be applied in other recommendation problems when various contextual information are involved.In the end, this dissertation concludes with a summarization of the research content and an outlook of future research opportunities and directions.