社交網(wǎng)絡中基于用戶偏好變化的影響最大化研究
發(fā)布時間：2018-11-12 18:53
【摘要】：隨著信息技術和互聯(lián)網(wǎng)技術的發(fā)展,諸如Facebook、微信等具有社交功能網(wǎng)站獲得了巨大的成功。影響最大化問題旨在挖掘社交網(wǎng)絡中最有影響力的Top-k個節(jié)點的集合,是社交網(wǎng)絡研究領域中的關鍵問題。但是一個用戶在一個社交網(wǎng)絡中可能對不同的話題感興趣,且偏好度不同,同時隨著時間的推移,用戶對話題的偏好度也會發(fā)生變化。在之前的很多工作中,影響最大化問題都忽視了這些因素,所挖掘的用戶都是全局模式下最有影響力的用戶。如果我們需要查找當前特定話題下最有影響力的用戶,傳統(tǒng)算法的精確度會受到影響。 在此背景下,本文提出了基于用戶偏好變化的影響最大化問題,建立了一個考慮用戶偏好變化的UCP_IC(Independent Cascade Model based on User Current Preferences)影響傳播模型。在UCP_IC模型中,為了解決用戶偏好變化問題,模型根據(jù)生物學中艾賓浩斯遺忘規(guī)律,設計了隨時間間隔遞減的指數(shù)函數(shù)來衡量用戶當前對話題的偏好。此外為了將用戶之間激活概率與用戶偏好關聯(lián)起來,我們同時考慮用戶之間的在特定話題下的聯(lián)系頻率與用戶對話題的偏好,并使用關聯(lián)規(guī)則的方法將兩者聯(lián)系起來作為用戶間激活概率。在此模型的基礎上,我們提出了GAUCP(Greedy Algorithm based on User Current Preferences)算法來挖掘當前在特定話題下最有影響力的用戶。該算法在考慮用戶當前偏好的情況下采用了貪心算法來挖掘用戶。在特定話題下,其能取得更好的精確度。基于影響傳播模型的子模特性,算法結果可以獲得約63%的精確度保證,并能使用CELF對算法計算效率進行優(yōu)化。 最后基于DBLP學術數(shù)據(jù)庫中相關數(shù)據(jù)進行了實驗,在特定話題下,GAUCP可以找到當前對話題最有影響力的用戶集。
[Abstract]:With the development of information technology and Internet technology, social networking sites such as Facebook, WeChat have achieved great success. Impact maximization is a key issue in the research field of social networks, which aims to excavate the set of the most influential Top-k nodes in social networks. However, a user may be interested in different topics in a social network, and their preferences may vary with the passage of time. In a lot of previous work, the problem of influence maximization ignores these factors, and the users mined are the most influential users in the global mode. If we need to find the most influential users on a given topic, the accuracy of traditional algorithms will be affected. Under this background, this paper proposes the problem of maximizing the influence of user preference change, and establishes a UCP_IC (Independent Cascade Model based on User Current Preferences) influence propagation model considering the change of user preference. In the UCP_IC model, in order to solve the problem of user preference change, according to the rule of Obinhos forgetting in biology, the exponential function of decreasing with time interval is designed to measure the user's current preference to the topic. In addition, in order to correlate the activation probability between users and user preferences, we also consider the frequency of contact between users under a specific topic and the user preference for the topic. The association rules are used to associate the two as the activation probability between users. Based on this model, we propose a GAUCP (Greedy Algorithm based on User Current Preferences) algorithm to mine the most influential users on specific topics. The greedy algorithm is used to mine users considering the current preferences of users. In a given topic, it can achieve better accuracy. Based on the influence of the submodel of the propagation model, the accuracy of the algorithm can be guaranteed by about 63%, and the computational efficiency of the algorithm can be optimized by using CELF. Finally, based on the relevant data in the DBLP academic database, GAUCP can find the most influential user set on the topic under the specific topic.
【學位授予單位】：云南大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：O157.5
本文編號：2327920
[Abstract]:With the development of information technology and Internet technology, social networking sites such as Facebook, WeChat have achieved great success. Impact maximization is a key issue in the research field of social networks, which aims to excavate the set of the most influential Top-k nodes in social networks. However, a user may be interested in different topics in a social network, and their preferences may vary with the passage of time. In a lot of previous work, the problem of influence maximization ignores these factors, and the users mined are the most influential users in the global mode. If we need to find the most influential users on a given topic, the accuracy of traditional algorithms will be affected. Under this background, this paper proposes the problem of maximizing the influence of user preference change, and establishes a UCP_IC (Independent Cascade Model based on User Current Preferences) influence propagation model considering the change of user preference. In the UCP_IC model, in order to solve the problem of user preference change, according to the rule of Obinhos forgetting in biology, the exponential function of decreasing with time interval is designed to measure the user's current preference to the topic. In addition, in order to correlate the activation probability between users and user preferences, we also consider the frequency of contact between users under a specific topic and the user preference for the topic. The association rules are used to associate the two as the activation probability between users. Based on this model, we propose a GAUCP (Greedy Algorithm based on User Current Preferences) algorithm to mine the most influential users on specific topics. The greedy algorithm is used to mine users considering the current preferences of users. In a given topic, it can achieve better accuracy. Based on the influence of the submodel of the propagation model, the accuracy of the algorithm can be guaranteed by about 63%, and the computational efficiency of the algorithm can be optimized by using CELF. Finally, based on the relevant data in the DBLP academic database, GAUCP can find the most influential user set on the topic under the specific topic.
【學位授予單位】：云南大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：O157.5
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相關期刊論文 前3條
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