隨著無線通訊技術、智慧型手持式裝置、定位系統以及社交網路系統的快速發展，基於位置的社群服務與應用像是朋友或被追蹤者推薦、地點推薦以及行動廣告，已經成為熱門的研究議題並且吸引許多研究學者的關注。因此，如何萃取、了解、分析以及利用從大量適地性社交網路資料中所獲得的移動行為知識，已經成為一個具有吸引力與挑戰性的議題。本研究旨在發展一系列新穎且有效率的資料探勘技術，從大量的適地性社交網路資料中挖掘有價值的知識，並且利用這些知識實現高品質的使用者移動預測、社交關係預測以及地點推薦等技術及應用。
首先，本研究探索了使用者移動行為之預測問題。在本問題之現存研究中，大多嘗試利用使用者移動軌跡找出人們移動的規律。這些研究認為人們的移動會遵循一定與地理空間資訊有關的規律，然而在一些研究中發現，人們的移動也會與時間資訊以及地區的語意資訊相關，而不是完全與地理空間資訊相關。因此，挖掘人們移動習慣上的語意資訊以及時間資訊將會有助於預測使用者的移動。為了能夠全方位的考慮地理上的、時間上的以及語意上的資訊來預測使用者的移動行為，一個能夠全方位探勘地理上的、時間上的以及語意上的資訊是必要的。基於以上的問題，本研究之第一部分即提出一個創新的使用者移動預測技術，透過分析使用者的移動軌跡，探勘出一種富含地理上的、時間上的以及語意上的資訊的樣式。利用此種樣式，建構一個準確預測使用者移動的模型。經由各種不同的參數實驗評估，此使用者移動預測技術上展現優異的效果。
其次，在社交行為探索的領域中，其中一個備受重視的議題為使用者朋友連結與追蹤連結探索與預測。大多數的研究基於所有的移動或社交行為紀錄，從中挖掘行為相像的使用者，並且利用這些相似度進行預測。然而這種模型的預測效果可能不夠準確，原因在於朋友連結與追蹤連結有著本質上的差異，朋友連結大多是與使用者的社群關係有關，即朋友的朋友也可能是朋友，但是追蹤連結則著重於資訊的分享，因此會使用者在社群網站分享的資訊才是人們決定是否追蹤的關鍵因素。因此一個好的模型必須要全方位的考慮到社群關係與分享資訊的語意相似度。有鑑於此，本研究之第二部分即提出一種創新的技術應用於社交行為探索與預測，透過適地性社群網路資料分析，有效地並且準確地預測使用者的社交行為。並且可以進行有效的朋友連結與追蹤連結推薦。
最後，在適地性網路蓬勃發展之後，打卡已經成為日常生活中一種不可或缺的行為。由於社群網站的普及，使用者越來越樂意彼此分享打卡資料。對於智慧型適地性有趣地點推薦，這樣的打卡資料是非常有價值。現今的適地性地點推薦系統通常會利用地理資訊系統與來了解使用者打卡地點的特性，進而了解使用者的偏好以利興趣點的推薦。然而日常生活中，地點通常是排列相當緊密的在一個地區，尤其是在都市之中，這樣的現象必會使利用地理資訊系統的方式失效。為了實現利用打卡資料了解使用者興趣並有效的推薦有趣地點給使用者，本研究之第三部分即發展一套創新的地點推薦技術，透過分析使用者的打卡資料，挖掘使用者的偏好以及社交特性，使推薦的興趣點更為符合使用者需要。經由各種不同的參數實驗評估，此興趣點推薦技術相對於向有技術展現優異的效果。

With the advance in wireless communication technologies, intelligent portable devices, location-acquisition availabilities and social network technologies, Location-Based Social Network System (LBSNs) have become the emerging research fields that attract a lot of attentions such as friend or followee recommendation, point-of-interest (POI) recommendation, location-based advertisement, etc. Hence, how to extract, understand, analyze and utilize the users’ behavior knowledge from such massive location-based social networks resources has become an attractive and challenging issue over the past few years. In this dissertation, we develop a series of efficient and effective data mining frameworks for discovering the valuable knowledge from location-based social networks to achieve high-quality next location prediction, social links prediction and POI recommendation.
First, we consider the problem of mining and prediction of users’ movement bahavior. Existing studies on location predictions mostly assumed that users’ movement should follow sorts of geographical regularity. However, some lectures address that users’ movement is also related to semantic or temporal regularities. To address the above issues, in the first part of this study, we propose a novel mining-based location prediction approach called Geographic-Temporal-Semantic based Location Prediction (GTS-LP), which takes into account a user’s geographical, temporal, and semantic regularity. The core idea underlying our proposal is the discovery of trajectory patterns of users to capture the three kinds of regularities. Through comprehensive evaluation on various real trajectory datasets, we show that our proposal delivers excellent performance and significantly outperforms existing state-of-the-art location prediction methods.
Second, we observe that researches on recommending friends and followees in social networks have attracted a lot of attentions in recent years. Existing studies on this topic mostly treat this kind of recommendation as just a type of friend of friend recommendation. However, apart from making friends, the reason of a user to follow someone is inherently to satisfy his/her information needs. Therefore, a better social link prediction should consider not only social relation but also semantic relation among users. Accordingly, in the second part of this study, we propose a novel social link prediction approach called Geographic-Social-Semantic based Friends and Followees Recommendation (GS2-F2R), which takes into account the user movements, online texting and social properties. Based on the similarity among users’ behavior, we make on-line recommendation for the followee a user might be interested in following, or friends a user would like to know.
Finally, because of the advanced development on location-based social network, lots of people perform the “check-in” for point-of-interests (POIs). Due to the rapid growth of social network website, more and more people like to share their location with their friends by performing check-in. As the result, such check-ins data is very useful for intelligent POI recommendation. However, traditional approaches always analyze users’ preference by overlapping users’ check-ins with the data from geographic information system. However, in real life, the POIs should be very crowded. This phenomenon must lead the approach by overlapping users’ check-ins with the data from geographic information system become useless. To realize analysis of users’ preference by their check-ins, in the third part of this study, we proposed a novel approach for POI recommendation called Dynamic HITS-Based Random Walk (DPOI-Walk), which takes into account a user’s social properties and personal preference to estimate the probability of a user checking-in to a POI. Through comprehensive evaluation on various real location-based social network datasets, we show that DPOI-Walk delivers excellent performance and significantly outperforms existing state-of-the-art POI recommendation methods.

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