【摘要】：隨著微博平臺中用戶的爆炸式增長，其用戶創(chuàng)造的信息也隨之呈指數(shù)級增長。從而導(dǎo)致過量的數(shù)據(jù)使得用戶無法有效地獲取自己想要的信息，即信息的使用率反而降低，信息過載的問題則日益加劇。目前的搜索引擎等技術(shù)只能滿足人們部分的需求，沒有個性化的考慮，仍無法有效地解決這個問題。用戶推薦作為一種信息過濾手段，是解決這個問題非常有潛力的方法。因而如何發(fā)展高效的，可擴展的，非常精確的用戶推薦算法是一個巨大的挑戰(zhàn)。 本文根據(jù)目前流行的微博平臺的特性提出了兩種用戶推薦算法，，一種是基于領(lǐng)域偏好度的名人推薦算法，另一種是基于社區(qū)信息傳播力的用戶推薦算法。基于領(lǐng)域偏好度的名人推薦算法將用戶推薦問題轉(zhuǎn)化為一個基于鏈接預(yù)測的分類問題，它基于名人用戶所屬的領(lǐng)域來圍繞目標用戶和被推薦名人用戶提取一系列的特征并以此構(gòu)建一個n維的特征向量，再利用分類器過濾有限的名人集合而得到該用戶的名人推薦集合。基于社區(qū)信息傳播力的用戶推薦算法則是基于社區(qū)劃分的思想，即將興趣相似的用戶聚到一個社區(qū)，通過分析該社區(qū)的消息流動情況，來挖掘社區(qū)中對消息傳播具有控制能力的消息中間人，同時結(jié)合目標用戶自身的特點從消息中間人中選取合適的用戶推薦給他。另一方面，為了解決當前海量數(shù)據(jù)處理的問題，本文針對兩種推薦算法還提出基于Map-Reduce的并行化實現(xiàn)方法。 通過在微博平臺數(shù)據(jù)集上的實現(xiàn)與測試，驗證了兩種推薦算法的可行性及有效性。根據(jù)推薦算法的一般評估方法，本文提出的兩種推薦算法與其它常用的推薦算法相比，效果均有所提高。同時基于Map-Reduce的并行化實現(xiàn)，算法性能明顯高于其單機環(huán)境。
[Abstract]:With the explosive growth of users in Weibo platform, the information created by its users has also increased exponentially. As a result, excessive data makes users unable to obtain the information they want effectively, that is, the utilization rate of information is reduced, and the problem of information overload is aggravated day by day. The current search engine and other technologies can only meet the needs of some people, without personalized consideration, still can not effectively solve this problem. As a kind of information filtering method, user recommendation is a potential method to solve this problem. Therefore, how to develop efficient, extensible, very accurate user recommendation algorithm is a huge challenge. According to the characteristics of Weibo platform, this paper puts forward two kinds of user recommendation algorithms, one is celebrity recommendation algorithm based on domain preference, the other is user recommendation algorithm based on community information transmission ability. The celebrity recommendation algorithm based on domain preference degree transforms the user recommendation problem into a classification problem based on link prediction. It extracts a series of features around the target user and the recommended celebrity user based on the domain to which the celebrity user belongs and constructs an n-dimensional feature vector. Then the classifier is used to filter the limited celebrity set to get the user's celebrity recommendation set. The user recommendation algorithm based on the ability of community information dissemination is based on the idea of community division, which brings users with similar interests to a community, and analyzes the information flow in that community. In order to mine the message middleman who has the ability to control the message propagation in the community, at the same time, combine the target user's own characteristic, select the appropriate user from the message intermediary to recommend to him. On the other hand, in order to solve the problem of mass data processing, this paper proposes a parallel implementation method based on Map-Reduce for two recommended algorithms. Through the implementation and test on Weibo platform data set, the feasibility and effectiveness of the two recommended algorithms are verified. According to the general evaluation method of the recommendation algorithm, the effect of the two recommendation algorithms proposed in this paper is improved compared with other commonly used recommendation algorithms. At the same time, the parallel implementation based on Map-Reduce shows that the performance of the algorithm is obviously better than that of its single computer environment.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP391.3;TP393.092

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