本文選題：用戶行為分析　切入點(diǎn)：偏好預(yù)測　出處：《北京郵電大學(xué)》2014年博士論文

【摘要】：互聯(lián)網(wǎng)與移動(dòng)通信技術(shù)的發(fā)展為各行各業(yè)帶來了創(chuàng)新熱潮,而隨著以用戶為中心理念的滲透,對用戶行為的分析和預(yù)測已經(jīng)成為提升用戶體驗(yàn)的重要手段。進(jìn)一步,各種社交網(wǎng)絡(luò)的流行及智能終端的普及,為分析和預(yù)測用戶的行為和偏好提供了海量的、真實(shí)的數(shù)據(jù)基礎(chǔ)。 然而,基于社交網(wǎng)絡(luò)的用戶行為分析和預(yù)測還存在一些問題需要解決。首先,在社交用戶交互網(wǎng)絡(luò)模型的構(gòu)建方面,包括用戶交互關(guān)系的生成和偏好一致性度量兩個(gè)問題。一方面,社交網(wǎng)絡(luò)中大部分用戶的社交關(guān)系是殘缺的、稀疏的,而這制約了用戶行為預(yù)測的覆蓋率和正確率。另一方面,用戶基于相似的興趣建立社交關(guān)系,然而,用戶之間的興趣存在差異。其次,社交影響作為社交網(wǎng)絡(luò)中一個(gè)重要的特征被廣泛的應(yīng)用于預(yù)測模型中。在社交網(wǎng)絡(luò)中,用戶的行為、觀念、看法、思想等,往往容易受到其社交關(guān)系的影響,而且這種社交影響還會(huì)沿著用戶之間的關(guān)系鏈進(jìn)行傳播,如著名的“三度影響力”理論。如何衡量社交網(wǎng)絡(luò)中用戶之間的社交影響以及計(jì)算社交影響的傳播是基于社交影響預(yù)測模型的關(guān)鍵。此外,如何從宏觀上和微觀上綜合對用戶行為偏好預(yù)測模型的預(yù)測結(jié)果進(jìn)行有效評估也是一個(gè)有意義的研究問題。 針對上述的用戶交互網(wǎng)絡(luò)構(gòu)建、用戶預(yù)測建模以及評估模型,論文圍繞社交網(wǎng)絡(luò)中的用戶行為偏好預(yù)測主題展開研究。借助用戶的圖相似性提取用戶的潛在關(guān)系,利用用戶的興趣數(shù)據(jù)設(shè)計(jì)有效的用戶偏好一致性度量算法。在用戶行為預(yù)測模型方面,對局部的、多樣性的用戶社交影響力計(jì)算模型進(jìn)行了研究,同時(shí),設(shè)計(jì)了一種可視化的、能夠從微觀層面評估預(yù)測算法性能的評估方法。本文的研究內(nèi)容及主要貢獻(xiàn)如下： 由于社交網(wǎng)絡(luò)的特性、用戶的時(shí)間和精力等因素的限制,用戶的社交關(guān)系往往是殘缺的。對于大多數(shù)用戶來說,其社交關(guān)系是非常稀疏的,導(dǎo)致了用戶社交交互網(wǎng)絡(luò)關(guān)系的殘缺,直接在殘缺的社交關(guān)系網(wǎng)絡(luò)上進(jìn)行預(yù)測會(huì)降低預(yù)測結(jié)果的覆蓋率和正確率�；诠�(jié)點(diǎn)相似度的方法是一種最簡單且流行的用戶關(guān)系挖掘方法,然而,其預(yù)測精度還有待于進(jìn)一步提高�？紤]到弱關(guān)系對于用戶的鏈接概率的重要作用,論文提出一種基于網(wǎng)絡(luò)節(jié)點(diǎn)中心性和弱關(guān)系理論的用戶潛在關(guān)系挖掘算法,以提升用戶缺失關(guān)系提取的準(zhǔn)確性。 社交網(wǎng)絡(luò)中,用戶興趣是用戶社交圈形成和維持的紐帶,對用戶偏好一致性的準(zhǔn)確度量,可以提升用戶行為預(yù)測的精度。而現(xiàn)有的用戶偏好相似性計(jì)算方法存在準(zhǔn)確性和區(qū)分度低等缺陷,不能很好的表征用戶之間的偏好相似性,因此,在現(xiàn)有方法基礎(chǔ)上,論文提出一種新穎的啟發(fā)式用戶相似性計(jì)算模型。該模型綜合考慮影響用戶偏好的微觀因素和宏觀因素,進(jìn)一步提升了用戶相似性的準(zhǔn)確性,并且使得用戶之間的相似性具有高度的可區(qū)分性。 社交影響及其傳播作為社交網(wǎng)絡(luò)的一個(gè)重要特性,得到了研究者的廣泛認(rèn)可和研究興趣。用戶的行為、思想、決策等經(jīng)常受到社交好友的影響,而且社交影響會(huì)沿著社交關(guān)系進(jìn)行傳播,通過對社交用戶之間的社交影響及其傳播的把握,可以幫助分析和預(yù)測用戶的行為趨勢。在社交影響計(jì)算方面,現(xiàn)有方法要么是缺乏多樣性的全局社交影響,要么需要知道網(wǎng)絡(luò)的整體信息。為此,論文分別提出一種基于局部節(jié)點(diǎn)網(wǎng)絡(luò)拓?fù)浜途植坑脩艚换サ纳缃挥绊懹?jì)算方法,該方法將社交影響的計(jì)算限制在單個(gè)用戶的鄰居范圍內(nèi),提取的社交影響是局部的、多樣的,而且計(jì)算量小,同時(shí)采用最短及最大傳播路徑策略來建模社交影響的傳播。 有效的評估模型可以幫助在不同場景下選擇最合適的預(yù)測算法,而大部分評估準(zhǔn)則僅僅給出一個(gè)綜合評估結(jié)果,并且建立在評估結(jié)果服從鐘形分布的基礎(chǔ)上,存在評估粒度不夠細(xì)、不能隨用戶體驗(yàn)粒度進(jìn)行調(diào)整等不足。本文研究發(fā)現(xiàn)很多預(yù)測結(jié)果的分布不符合鐘形分布,而是近似服從一種冪率分布。因此,論文提出了一種累積概率分布模型的可視化評估方法,可以從更加細(xì)粒度的層面對不同的預(yù)測算法進(jìn)行對比。同時(shí)基于該累積概率分布計(jì)算評估期望值,這樣能夠根據(jù)用戶體驗(yàn)的粒度對預(yù)測結(jié)果進(jìn)行離散化處理,因此計(jì)算得到的評估期望更加符合用戶真實(shí)的體驗(yàn)和場景需求。
[Abstract]:The development of Internet and mobile communication technology have brought innovation boom for all walks of life, and with the development of user centered concept, analysis and prediction of user behavior has become an important means to enhance the user experience. Further, the popularity of social networks and intelligent terminals, providing analysis and predict the behavior of users and preference for massive, real data based.
However, user behavior analysis and prediction based on social network there are still some problems to be solved. First of all, in the construction of social network user interaction model, including measurement of two problems of user interaction between generation and preference consistency. On the one hand, most of the social network of users of social relations is incomplete, sparse but, this restricts the coverage of user behavior prediction and correct rate. On the other hand, similar user interests based on social relationship, however, there are differences between the user interest. Secondly, social influence as an important feature of the social network is widely used in the prediction model. In social networks, user behavior, ideas, opinions, ideas and so on, are easy to be affected by the social relations, and the social impact will be along the chain relationship between users of communication, such as the famous "three degrees. The force "theory. How to measure the effect of social communication between users in a social network and the social influence is the key calculation prediction model based on social influence. In addition, how to predict from the macro and micro integration of user behavior preference prediction model results in effective evaluation is a significant research problem.
According to the construction of network user interaction, the user modeling and evaluation model, based on the preferences of the user behavior in social network prediction themes of study. Through the user's relationship to extract the user map of the potential similarity metric algorithm based on user preference data user interest to design effective consistency. Prediction model in terms of user behavior, to part of the diversity of users of social influence model is studied, and a visual design, a method to evaluate the algorithm performance evaluation from the micro level. The research content of this paper and the main contributions are as follows:
Due to the characteristics of social network users, such as time and energy constraints, the user's social relationship is often incomplete. For most users, the social relationship is very sparse, leading to users of social interaction network is incomplete, direct relations network forecast will reduce the coverage prediction results and correct the rate of incomplete agency. Method based on node similarity mining method, one of the most simple and popular user relationship however, the prediction accuracy remains to be further improved. Considering the important role of weak ties for the link probability of users, this paper proposes a algorithm for mining user potential relationship network centrality and weak relationship based on the theory, to enhance the user accuracy. The lack of relation extraction
Social network users, user interest is the social circle to form and maintain ties, to accurately measure the consistency of user preferences, user behavior can enhance the prediction accuracy and user preference. The existing similarity calculation method in accuracy and discrimination of low defect, the user can not be a good characterization between the preference similarity, therefore, based on the existing method, this paper proposes a similarity calculation model and a heuristic novel. The model considering the micro factors and macro factors affecting user preferences, to further improve the accuracy of user similarity, and the similarity between users is highly distinguishable.
Social influence and communication as an important characteristic of the social network, has been widely recognized by researchers and research interest. The user's behavior, thinking, decision-making is often influenced by social friends, and social influence will spread along the social relations, the social impact of social and communication between users to grasp the behavior trend analysis and forecasting can help users. In the calculation of social effects, existing methods either lack of global social effects of diversity, or need to know the whole information network. Therefore, put forward a calculation method of the effect of local node network topology and user interaction based on the local social respectively, the method of calculating social influence Limited to a single user's neighbors within the scope of social impact extraction is local, diversity, and a small amount of calculation, the shortest and the maximum propagation The path strategy is used to model the communication of social impact.
The model can help to choose the most suitable prediction algorithm in different scenarios and the effective evaluation and evaluation criteria given only a most comprehensive evaluation results, and based on evaluation results based on the distribution of the bell to assess the size, fine enough, not enough with the user experience granularity adjustment. This study found that many predicted distribution the results do not conform to the bell shaped distribution, but approximately obey a power-law distribution. Therefore, this paper proposes a visual assessment method of cumulative probability distribution model, can be compared with different prediction algorithms from a more fine-grained layer. At the same time the evaluation of expected value of cumulative probability distribution based on this, according to the size of the user experience the forecast results are discretized, therefore the calculated expected assessment more in line with the user experience and the real needs of the scene.

【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TP393.09

【參考文獻(xiàn)】

相關(guān)期刊論文 前3條

1 呂琳媛;;復(fù)雜網(wǎng)絡(luò)鏈路預(yù)測[J];電子科技大學(xué)學(xué)報(bào);2010年05期

2 朱郁筱;呂琳媛;;推薦系統(tǒng)評價(jià)指標(biāo)綜述[J];電子科技大學(xué)學(xué)報(bào);2012年02期

3 劉建國;周濤;郭強(qiáng);汪秉宏;;個(gè)性化推薦系統(tǒng)評價(jià)方法綜述[J];復(fù)雜系統(tǒng)與復(fù)雜性科學(xué);2009年03期

相關(guān)博士學(xué)位論文 前1條

1 吳銘;基于鏈接預(yù)測的關(guān)系推薦系統(tǒng)研究[D];北京郵電大學(xué);2012年

，

本文編號：1690991