隨著 Web3.0的火熱興起，社交網路的發展異常火熱。人人網，微博，Qzone等社交網路的快速發展吸引了大批量的用戶。近幾年，在國內社交網路方面，微博的快速發展和它獨有的開放性、草根性、即時性模式吸引了大量的使用者。越來越多的人通過微博獲取即時的資訊來瞭解身邊、社會、甚至世界發生的事情。但是隨著微博用戶的越來越多，微博上面每天有數億計的微博內容產生，微博使用者面臨著嚴重的“資訊超載”現象。如何讓微博使用者從“資訊超載“的環境中脫離出來，從而在微博網路中找到使用者感興趣的用戶，通過找到他們感興趣的用戶來使他們獲得他們感興趣的資訊資訊是一個值得研究的問題。這對於微博用戶提升使用體驗，快速獲得需要的資訊都是非常重要的。

在本篇論文中，我們研究了微博社交網路平臺。在這其中我們主要分析並研究了基於微博平臺用戶的個性化推薦，設計並完善了使用者個性化推薦演算法。其中運用了LDA主題模型進行了使用者主題模型的建構以及使用者興趣分佈的預測。從結果上來看，LDA主題模型的加入對於推薦使用者準確性的提升有較大幫助。

With the recent rise of fiery web3.0, the development of the social network is very hot. The rapid development of the social network such as RenRen, QZone, micro-blog, Facebook have attracted large number of users. In recent years, in terms of domestic social network, the micro-blog is very fascinating. More and more people know the news nearby, society, the world by the real-time information on micro-blog platform. But with the increasing number of the micro-blog users, there is billions of micro-blog information every day. The micro-blog users face the serious phenomenon of "information overload". How to let a micro-blog user get away from the phenomenon and find the information that interested in the micro-blog through the users that interested in is a problem worthy of studying. This study is very important for the improvement of users' usage experience and the quick access to information.

In this thesis, we research an important aspect of social network micro-blog, and we mainly analyze and study the personalized recommendation problem on micro-blog users. Research and design the user personalized recommendation algorithm. Therein, we use LDA to construct the topic model of users and predict the interest distribution of target users. The result shows that it has a improvement for the accuracy of users prediction to user LDA topic model.

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