發(fā)布時間：2018-04-28 21:34

  本文選題：社交網(wǎng)絡(luò) + 虛擬用戶　； 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：社交網(wǎng)絡(luò)平臺作為一種用戶交流和分享信息的虛擬平臺,蘊含了大量的用戶信息,其中用戶屬性信息在個性化推薦、精準(zhǔn)營銷以及輿情引導(dǎo)等方面發(fā)揮著重要作用。社交網(wǎng)絡(luò)平臺無法準(zhǔn)確獲取用戶屬性,就不能為用戶提供優(yōu)質(zhì)服務(wù)。因此,研究用戶屬性對社交網(wǎng)絡(luò)平臺具有重要作用。該領(lǐng)域不同研究者對虛擬用戶屬性定義不一致,導(dǎo)致在研究屬性推測問題時,存在結(jié)構(gòu)混亂(父子級關(guān)系不明確)、推測方法適用性受限、研究成果可拓展性差等問題。針對這些問題,本文對虛擬用戶屬性定義以及推測方法進行了深入研究。首先,針對虛擬用戶屬性定義不一致的問題,研究了虛擬用戶屬性表述模型。通過分析社交網(wǎng)絡(luò)用戶數(shù)據(jù),刻畫了描述用戶的四個屬性維度。同時,針對屬性缺失及不準(zhǔn)確的問題,提出了一種基于模型分類以及鄰居-社團聯(lián)合更新的虛擬用戶屬性計算模型。該模型能針對不同基本屬性特點,控制更新操作的有無,在節(jié)約時間成本的同時,提高了屬性推測的準(zhǔn)確率。其次,將虛擬用戶屬性計算模型應(yīng)用于具體基本屬性推測任務(wù)。針對屬性聚集性強弱的差異,進行了性別和職業(yè)兩種屬性的研究。在性別推測中,針對性分析了用戶使用習(xí)慣特征,提出了特征選擇及基于字典的特征權(quán)重計算方法,并引入了一種基于樸素貝葉斯融合的分類算法,彌補了單純特征疊加效果不佳的問題,提升了分類效果;在職業(yè)推測中,改進了現(xiàn)有的主題式特征選擇方法,設(shè)計了基于更新機制的分類算法,并測試分析了算法中的影響因子。實驗證明,該算法在應(yīng)用于職業(yè)推測時,能有效提升分類效果。最后,基于上述研究設(shè)計了虛擬用戶屬性推測原型系統(tǒng)。該系統(tǒng)通過分析待測用戶發(fā)布內(nèi)容、鏈接關(guān)系數(shù)據(jù),獲取自身以及鄰居-社團傳播的信號,最終推測用戶的屬性類別,從而用于充實用戶屬性表述模型,達到提升個性化推薦服務(wù)質(zhì)量的目的。
[Abstract]:As a virtual platform for users to exchange and share information, social network platform contains a lot of user information, among which user attribute information plays an important role in personalized recommendation, accurate marketing and public opinion guidance. Social network platform can not accurately obtain user attributes, can not provide quality services for users. Therefore, the study of user attributes plays an important role in the social network platform. Different researchers in this field have different definitions of virtual user attributes, which lead to the confusion of structure (the relationship between father and son is unclear, the applicability of speculation method is limited, and the scalability of research results is poor). In order to solve these problems, the definition of virtual user attributes and the methods of conjecture are studied in this paper. Firstly, aiming at the problem of inconsistent definition of virtual user attributes, a virtual user attribute representation model is studied. By analyzing the data of social network users, the four attribute dimensions of describing users are described. At the same time, a virtual user attribute computing model based on model classification and joint update of neighborhood and community is proposed to solve the problem of missing and inaccurate attributes. According to the characteristics of different basic attributes, the model can control the availability of update operation and improve the accuracy of attribute speculation while saving time and cost. Secondly, the virtual user attribute calculation model is applied to the specific basic attribute estimation task. Aiming at the difference of attribute aggregation, gender and occupation are studied. In gender speculation, the characteristics of users' usage habits are analyzed, and a method of feature selection and feature weight calculation based on dictionary is proposed, and a classification algorithm based on naive Bayes fusion is introduced. It makes up for the problem of poor superposition effect of pure features and improves the classification effect. In career speculation, it improves the existing thematic feature selection methods, and designs a classification algorithm based on updating mechanism. The influence factors of the algorithm are tested and analyzed. Experiments show that the algorithm can effectively improve the classification effect when it is applied to occupational speculation. Finally, a virtual user attribute inference prototype system is designed based on the above research. By analyzing the content of the users to be tested, linking the relational data, acquiring the signals propagated by themselves and the neighbors and communities, the system finally inferred the user's attribute category, which was used to enrich the user attribute representation model. To improve the quality of personalized recommendation service.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP393.09

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本文編號：1816999