本文選題：新浪微博 + 好友推薦　； 參考：《燕山大學(xué)》2014年碩士論文

【摘要】：隨著信息技術(shù)飛速發(fā)展及互聯(lián)網(wǎng)的迅速普及，微博以其新穎的形式走入網(wǎng)民的視線，并迅速走紅。2009年新浪推出“新浪微博”內(nèi)測(cè)版，微博正式進(jìn)入中文上網(wǎng)主流人群視野，并迅速發(fā)展，在國(guó)內(nèi)引發(fā)了信息傳播的“微革命”。隨著微博在網(wǎng)民中日益火熱，好友推薦成為了用戶關(guān)注的焦點(diǎn)，同時(shí)也是本文研究的重要內(nèi)容。 首先，分析好友推薦算法的研究現(xiàn)狀、核心思想以及目前存在的問(wèn)題與不足。在此基礎(chǔ)上，設(shè)計(jì)了基于用戶的協(xié)同過(guò)濾好友推薦算法。該算法主要包括興趣愛(ài)好、住址、二度人脈、教育工作信息和綜合因素五個(gè)部分。 其次，為前四個(gè)部分建立相似關(guān)系，通過(guò)關(guān)系算法公式分別計(jì)算四個(gè)部分的相似度得分，再為每個(gè)部分設(shè)置不同的權(quán)重。綜合四部分的相似度得分計(jì)算出每個(gè)推薦結(jié)果的得分，根據(jù)分?jǐn)?shù)篩選，，將綜合相似度最高的前幾項(xiàng)及其相似度分別顯示給用戶。 最后，設(shè)計(jì)了新浪微博好友推薦系統(tǒng)，完成了對(duì)系統(tǒng)總體功能的設(shè)計(jì)。通過(guò)對(duì)注冊(cè)登錄、個(gè)人主頁(yè)、個(gè)人資料、個(gè)人微博、個(gè)人相冊(cè)、關(guān)注粉絲、分組管理及好友主頁(yè)等功能模塊設(shè)計(jì)相關(guān)數(shù)據(jù)庫(kù)，實(shí)現(xiàn)了新浪微博好友推薦系統(tǒng)，并通過(guò)用戶數(shù)據(jù)來(lái)驗(yàn)證了好友推薦算法的有效性，從而實(shí)現(xiàn)了快速幫助用戶篩選有效信息的目標(biāo)。
[Abstract]:With the rapid development of information technology and the rapid popularization of the Internet, Weibo has come to the attention of Internet users in its novel form, and has quickly become popular. In 2009, Sina launched the "Sina Weibo" internal version, and Weibo formally entered the mainstream Chinese Internet view. And rapid development, in the domestic triggered the information dissemination of the "micro-revolution." With the increasing popularity of Weibo among Internet users, friend recommendation has become the focus of user attention, but also an important part of this paper. Firstly, the research status, core idea, problems and shortcomings of friend recommendation algorithm are analyzed. On this basis, a collaborative filtering algorithm for friend recommendation based on user is designed. The algorithm mainly includes five parts: interest, address, second degree network, educational work information and comprehensive factors. Secondly, the similarity relation is established for the first four parts, the similarity score of the four parts is calculated by the formula of the relational algorithm, and the weight of each part is set up. By synthesizing the similarity scores of the four parts, the score of each recommendation result is calculated. According to the score selection, the first items with the highest synthetic similarity and their similarity are displayed to the users respectively. Finally, the Sina Weibo friend recommendation system is designed, and the overall function of the system is designed. By designing the relevant database for the function modules of registration login, personal home page, personal data, personal Weibo, personal photo album, attention to fans, group management and friend home page, the paper realizes the Sina Weibo friend recommendation system. The validity of the friend recommendation algorithm is verified by user data, and the goal of quickly helping users to filter effective information is achieved.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP391.3;TP393.092

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