本文關鍵詞： 社會化網(wǎng)絡 用戶關系強度 活動領域分類 直接關系 間接關系　出處：《浙江工商大學》2017年碩士論文　論文類型：學位論文

【摘要】：隨著互聯(lián)網(wǎng)的發(fā)展,社會化網(wǎng)絡迅速流行,并在人們的日常生活中發(fā)揮著至關重要的作用,為信息傳播、經驗分享、生活交流等活動開拓了重要渠道。也因此,社會化網(wǎng)絡中用戶間的關系強度引起了研究者的高度重視。社會化網(wǎng)絡在多個領域的重要作用逐漸凸顯,如應用于好友推薦、商品推薦、鏈路預測等。就個性化服務推薦而言,社會化網(wǎng)絡用戶間的關系強度是進行推薦的重要依據(jù)。目標推薦用戶的喜好往往跟與他有較強關系強度的用戶更接近,而來自具有親密關系的人的推薦也往往是更容易被接受的。因此,社會化網(wǎng)絡中用戶間關系強度的重要性不言而喻。但目前已有的關系強度計算方法考慮都較為片面,許多研究只是籠統(tǒng)地對社會化網(wǎng)絡中用戶間的關系強度進行計算,而未針對特定的情況進行研究,并且許多研究只針對社會化網(wǎng)絡中用戶間存在的直接關系進行研究,而忽略具有舉足輕重的間接關系,因此計算結果的精確度有待提高�；谝陨蠁栴},本文提出了一種基于活動領域分類與間接關系融合的社會化網(wǎng)絡用戶關系強度計算模型,主要研究內容主要包括以下幾個方面:第一,通過爬蟲獲取社會化網(wǎng)絡中的相關數(shù)據(jù),對數(shù)據(jù)進行預處理(包括中文分詞、去停用詞),轉化為相應的文檔數(shù)據(jù)集,去除垃圾數(shù)據(jù),有助于計算結果準確性的提高。第二,對社會化網(wǎng)絡中用戶群的交互活動進行活動領域分類。用LDA算法對用戶交互活動文檔進行集群,利用標準化谷歌距離將結果集群與活動領域名稱(工作、飲食、購物、旅游、運動、娛樂)進行相關度計算,確定每個結果集群所屬的活動領域。之后再進一步通過相關度的計算判斷每個交互活動文檔所屬的活動領域。結合活動領域分類對社會化網(wǎng)絡中用戶間的關系強度進行計算有助于該研究成果后續(xù)能更有針對性地應用于其他領域,如應用于個性化推薦時,可以分領域進行推薦,提高推薦的成功率。第三,直接關系強度計算中充分考慮多種影響因素。結合個體相似性、時間性、互動性對每個交互活動領域內用戶間的直接關系強度進行計算,充分考慮了多方面的關系強度影響因素,有利于直接關系強度的準確計算。第四,融合間接關系于關系強度計算過程中。考慮到間接關系在社會化網(wǎng)絡關系網(wǎng)中具有舉足輕重的地位,在最終關系強度的計算中融合了間接關系,不僅解決了不存在直接關系而只存在間接關系的用戶間關系強度無法計算的問題,而且提高了關系強度計算的準確性。第五,提出了衡量關系強度計算結果準確性的評價指標。分別與基于文檔級別、集群級別、微博會話的活動領域分類方法比較,評價本文所提出的活動領域分類方法的效率。并根據(jù)準確率、召回率和標準衡量搜索引擎質量指標(NDCG)作為實驗結果的評價指標,將本文所提出的關系強度計算方法分別與線性組合方法、通用框架模型方法比較,實驗結果表明本文所提的基于活動領域分類與間接關系融合的社會化網(wǎng)絡用戶關系強度計算方法更優(yōu)。
[Abstract]:With the development of the Internet, the rapid popular social network, and in people's daily life plays a vital role in sharing the experience for the dissemination of information, communication and other activities, life opens up an important channel. Therefore, the strength of relationship between users in social network have attracted the attention of researchers in social networking. An important role in many fields gradually highlighted, as for friend recommendation, recommendation, link prediction. The personalized service recommendation, the strength of the relationship between social network users is an important basis for the recommended target. Recommended user preferences tend to have strong relationship with his strength and is closer to the user. From people with intimate relations recommended also tend to be more easily accepted. Therefore, it is self-evident importance of relationship strength between users in social network. But the existing intensity meter Considering the calculation method are relatively one-sided, many studies only loosely on social relationship strength between users in the network are calculated, but not for the specific case study, and many studies are focused on the direct relationship between social network among users of neglect has indirect relationship important, therefore the calculation results the accuracy needs to be improved. Based on the above problems, this paper presents a computational model of social network user relationship strength fusion classification based on field of activity and the indirect relation, the main research contents include the following aspects: first, access to relevant data in social network by crawler, data preprocessing (including Chinese segmentation, go to stop words), into the corresponding document data set, remove garbage data, help to improve the accuracy of the calculation results. Second, on the social network Interactive activities in the user group of activities in the field of classification. Cluster of user interaction activities document with the LDA algorithm, using standard Google distance name cluster and field results (work, food, shopping, travel, sports, entertainment) related calculation, determine the result of each cluster belongs to the field of activity. Further through calculation of the correlation judgment of each document are interactive activities activities. Combining activities in the field of classification of relationship strength between users in the network of social computing is helpful for the subsequent research results can be more targeted application in other fields, such as for personalized recommendation, can be divided into the field of recommendation and recommended to improve success rate. Third, a variety of factors considered in the calculation of direct relationship strength. Combined with the individual similarity, timeliness, interaction of each interaction in the field of use Calculate direct relationship strength between households, fully considering the factors of relationship strength to many factors, there are conducive to the accurate calculation of direct relationship strength. In fourth, the indirect relationship between fusion relationship strength calculation process. Considering the indirect relationship plays an important role in the social relationship network, in the calculation of the final strength of the relationship fusion of indirect relationship, not only solved there is no direct relationship between strength can not be calculated only indirect relation between users, but also improve the accuracy of relationship strength calculation. Fifth, put forward the measure of relationship strength calculation accuracy evaluation index. And based on the document level, cluster level, micro-blog session activities the field classification method, efficiency evaluation of this field of activity. According to the classification accuracy rate, recall rate and standard search engine The quality index (NDCG) was used as the index to evaluate the experimental results, the method with linear combination method of strength calculation of the relationship will be presented in this paper, the comparative method of general framework model, the experimental results show that the social network user relationship strength fusion classification based on field of activity and the indirect relationship between the proposed calculation method is better.

【學位授予單位】：浙江工商大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP393.09

【參考文獻】

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本文編號：1506894