隨著社群網路的興起，從社交媒體資料中探勘有用的資訊已成為熱門的研究方向。過去與社群網站相關的研究，如興趣點（point of interest, POI）推薦及打卡地點預測，主要著重於二元的朋友關係，使用者之間的社交關係強弱卻常常被忽略。一般而言，人們的決策常常受到摯友影響。然而，二元的朋友關係卻無法明確指出兩人之間的親密度。此外，若將使用者在社群網站上所有的朋友納入分析，由於點頭之交也被視為與摯友同等重要，將引入大量不相關之資料與雜訊。我們認為這是過去相關研究無法有效利用社交資料協助分析使用者行為的主因之一，若能更深入探討社交關係強弱所帶來的影響並加入分析，將更能反映現實情況，並有機會改善相關研究的成果。因此，在本論文中，我們著重分析基於地點之社群網路（location based social network, LBSN）中目標使用者與他人間的社交關係強弱。透過使用者的打卡（check-in）狀況以及打卡地點的特性以分析使用者之間的社交關係，並以此為候選地點進行加權。最後考慮候選地點的距離以及他人在此地打卡的次數對目標使用者的影響力，以獲得較為全面的地點推薦分數。在此架構下，與使用者關係越親密的朋友將獲得更高的權重，而其經常拜訪的地點也將獲得更高的分數。最後，我們藉由真實資料對此系統進行分析驗證。

With the emergence of social networks, mining interesting information from the social media datasets becomes a popular research direction. Previous re-searches on social networks, such as POI (point of interest) recommendation, usually ignore the social tie strength between users. If we can further consider the closeness between friends in the analysis, it is possible to improve the results. Therefore, in this paper, we focus on analyzing the social tie strength between users in the location-based social network. The proposed method analyzes the movement of users, the interaction between them and location properties by the spatial-temporal data. Furthermore, we take into account the geographical feature and the influence of check-in times. Finally, the location list for POI recommendation will be constructed accordingly. Experimental results show that the proposed method significantly outperforms the competitor on both the precision and the recall.

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