本文關(guān)鍵詞：基于位置社交網(wǎng)絡的用戶行為建模與研究　出處：《中國科學技術(shù)大學》2017年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 位置社交網(wǎng)絡 用戶行為偏好 興趣點推薦 地點預測 表示學習

【摘要】：近年來,隨著移動互聯(lián)網(wǎng)的快速擴展和定位技術(shù)的日趨成熟,與位置社交網(wǎng)絡相關(guān)的服務平臺和信息被廣泛應用于生活中。位置服務的廣泛應用使得大量的位置數(shù)據(jù)得以積淀下來,這為挖掘位置數(shù)據(jù)背后用戶的行為偏好提供了有力的支撐。通過分析用戶的行為偏好,所構(gòu)建的位置社交平臺可以更好地便利人們的生活與出行,同時有關(guān)于用戶偏好的分析結(jié)果也可以給予商家和相關(guān)行業(yè)的決策者更有益的建議和指導。因此,本文的工作重點是從現(xiàn)在和未來兩個角度出發(fā),挖掘和分析用戶的行為偏好,從而進行興趣點推薦和位置預測。雖然位置社交網(wǎng)絡提供了豐富的位置數(shù)據(jù)來源,但是位置數(shù)據(jù)本身的異構(gòu)性和稀疏性等特點給現(xiàn)有的推薦和預測方法帶來了諸多挑戰(zhàn)。針對位置數(shù)據(jù)的這一系列特點和存在的挑戰(zhàn),本文分別提出了相應的方法來更好地應對在推薦和預測問題建模過程中遇到的相關(guān)情況。具體來說包含以下兩個方面:1.針對興趣點推薦問題,本文構(gòu)建了一個基于多源異構(gòu)信息的混合興趣點推薦模型。位置社交網(wǎng)絡中蘊含著豐富的實體和關(guān)聯(lián)關(guān)系,體現(xiàn)在位置數(shù)據(jù)上就是豐富的多源異構(gòu)信息。通過合理的建模和算法設計來有效地整合這些信息可以改善興趣點推薦的實際效果。針對位置社交網(wǎng)絡中的多源異構(gòu)信息,本文提出了一種基于用戶虛擬興趣和現(xiàn)實距離相結(jié)合的混合興趣點推薦方法。具體來說,本文采用核密度估計的方法對地理空間距離來進行度量,使用基于好友和有共同簽到地點的用戶的協(xié)同過濾方法來衡量好友和興趣相似的其他用戶對于用戶本身對興趣點的心理認同度的影響,同時使用基于用戶和興趣點文本聚集的概率話題模型來挖掘用戶和興趣點的偏好,從而對用戶虛擬興趣中可解釋的部分進行建模。相應的,本文使用概率隱因子模型對用戶虛擬興趣中不可解釋的部分加以建模。最終本文將上述子模塊有機地結(jié)合起來得到混合興趣點推薦模型。本文在兩個典型的位置數(shù)據(jù)集上進行了充分的實驗,實驗結(jié)果表明本文提出的混合興趣點推薦算法優(yōu)于當前已有的興趣點推薦算法。此外,模型還具有更準確的預測性和很好的健壯性等優(yōu)勢。2.針對地點預測問題,本文提出了一種基于簽到序列的隱話題向量位置預測模型。研究表明,位置社交網(wǎng)絡中用戶的行為偏好具有很強的規(guī)律性和可預測性,并且和用戶與地點所在的情境密切相關(guān)。對于大多數(shù)用戶來說,其簽到記錄相比于整個數(shù)據(jù)的分布而言具有很強的稀疏性。因此如何針對位置數(shù)據(jù)的上述特點構(gòu)建預測模型來進行地點預測是一個亟待解決的重要問題。本文提出了一種基于簽到序列的隱話題模型。具體來說,對于位置社交網(wǎng)絡中的地理空間信息,本文采用基于區(qū)域的高斯分布模型進行建模。為了緩解社交關(guān)系稀疏性對預測結(jié)果的影響,本文對用戶的社交關(guān)系進行了擴展。同時本文把基于上下文的詞向量模型和基于時間的主題模型結(jié)合起來,構(gòu)建隱話題向量模型來對用戶簽到行為的情境進行建模。對于其簽到的規(guī)律性行為,本文對連續(xù)時間進行了橫向與縱向的分割,把連續(xù)時間離散化。綜合上述建模方法可以得到用戶在不同時間模式下的興趣偏好表示以及地點的表征向量,從而有效地預測下一時間模式下用戶訪問的地點。本文在典型的位置數(shù)據(jù)集上的實驗結(jié)果表明與傳統(tǒng)的地點預測方法相比,本文提出的模型具有更高的準確性。
[Abstract]:In recent years, with the rapid expansion of the Internet and mobile positioning technology is becoming more and more mature, and the position related social network service platform and information is widely used in daily life. Widely used location service that the position data settled and provides a strong support for the behavior preference mining. Through the data behind the user location analysis of user preferences, social position of the platform can better facilitate people's life and travel at the same time, there are more useful advice and guidance on user preference results can also be given to related businesses and industry decision makers. Therefore, the emphasis of this paper is to start from now and in the future two aspects of mining and analysis of user behavior, so as to predict the point of interest and recommended position. Although the location of social network provides a rich source of data location, but The location of the data itself characteristics of heterogeneous and sparseness and recommend the existing prediction methods have brought many challenges. For this series of characteristics of the position data and the challenges, this paper puts forward the corresponding methods to better respond to the relevant circumstances encountered in the process of the prediction and recommendation problem specifically includes modeling. The following two aspects: 1. to the point of interest problems is recommended, this paper constructs a hybrid recommendation model of multi-source heterogeneous information based on social network position. Points of interest are rich in entity and relationship, reflected in the position of the data is rich in multi-source heterogeneous information. Through the modeling and design of reasonable algorithm to effectively integrate this information can improve the actual effect of interest recommendation. For multi-source heterogeneous information position in social networks, this paper proposes a method based on user interest and virtual The real distance of combining interest recommendation methods. Specifically, this paper uses the method of kernel density estimation for spatial distance measurement, based on the use of friends and common collaborative filtering method to measure the user sign in place of friends and other users with similar interests to the user itself to the point of interest is the influence of psychological identity at the same time, the use of probabilistic topic model and user interest aggregation to text mining user preferences and points of interest, in order to model the interpretation of part of the user interest. The corresponding virtual modeling of virtual users, it can not explain the interest in this part of the use of probabilistic latent factor model. Finally the sub module organically mixed interest recommendation model. This paper has carried on the experiment in two typical position data sets, experimental results table The proposed hybrid algorithm is better than the current recommended interest interest recommendation algorithm. In addition, the model has more accurate prediction and good robustness for the.2. advantage location prediction problem, this paper proposes a prediction model based on hidden topic vector position sign sequence. The results show that the position of the user in a social network behavior preference has strong regularity and predictability, and closely related to the user's location and context. For most users, the attendance record distribution compared to the entire data sparsity has very strong. So how to according to the characteristics of the position data prediction model is constructed to place forecasting is an important problem to be solved. This paper proposes a topic model based on implicit sign sequence. Specifically, the position of social networks in the geographical space The information modeling of Gauss distribution model based on region. In order to alleviate the impact of social relations the sparsity of the forecast results, the relationships of the users of the expansion. At the same time the word context vector model and topic model based on time based on user behavior, to construct the context modeling sign the hidden topic vector model. For the regularity of the sign of sexual behavior, the horizontal and vertical segmentation of continuous time, continuous time discretization. The modeling method can get the user preference vector characterization in different time under the mode of representation and location, which can effectively predict the next time the user mode to access a location. Based on the position data of the typical set of experimental results show that compared with the traditional location prediction method, the model proposed in this paper. There is a higher accuracy.

【學位授予單位】：中國科學技術(shù)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.3

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本文編號：1386247