本文選題：推薦系統(tǒng) + 圖模型��； 參考：《山東農(nóng)業(yè)大學(xué)》2017年碩士論文

【摘要】：近年來,互聯(lián)網(wǎng)尤其是移動互聯(lián)網(wǎng)規(guī)模和技術(shù)發(fā)展迅猛,智能移動設(shè)備如智能手機(jī)、平板電腦等大量普及,智能手機(jī)用戶數(shù)量劇增。移動應(yīng)用作為智能手機(jī)的重要組成部分,改變了用戶的生活方式、工作方式、學(xué)習(xí)方式等,在系統(tǒng)設(shè)計與開發(fā)中占有重要地位。移動應(yīng)用市場如Google Play Store、Apple App Store等中的移動應(yīng)用數(shù)量達(dá)到了百萬級別,海量的移動應(yīng)用在給用戶提供便利、開發(fā)人員提供參考的同時,也帶來了新的挑戰(zhàn)。從用戶角度來看,在海量應(yīng)用中找到自己感興趣的應(yīng)用面臨巨大困難;從開發(fā)人員角度來看,在海量應(yīng)用中選取合適的應(yīng)用作為參考耗費大量精力。與此同時,智能手機(jī)用戶低齡化是一個不可忽視的趨勢,如何防止低齡用戶沉迷于智能手機(jī)是一個亟待解決的問題。本文對移動應(yīng)用個性化推薦進(jìn)行研究,首先對推薦算法進(jìn)行了改進(jìn),提出了一種基于用戶分裂的資源擴(kuò)散算法。在調(diào)研個性化推薦算法的過程中,發(fā)現(xiàn)用戶在時間維度上積累的興趣偏移對推薦算法的準(zhǔn)確率影響很大。在基于圖模型的資源擴(kuò)散算法的基礎(chǔ)上,將時間上下文信息融入算法中,提出了一種基于用戶分裂的資源擴(kuò)散算法,改進(jìn)了傳統(tǒng)的資源擴(kuò)散算法。改進(jìn)算法把興趣隨時間發(fā)生變化的用戶看作多個用戶,即采用用戶分裂思想引入時間上下文信息,更加充分的利用了數(shù)據(jù)信息,通過實驗對比發(fā)現(xiàn),和傳統(tǒng)的資源擴(kuò)散算法相比,改進(jìn)算法的準(zhǔn)確率明顯提高。然后將基于用戶分裂的資源擴(kuò)散算法應(yīng)用于移動應(yīng)用推薦領(lǐng)域,設(shè)計了一種面向智能手機(jī)用戶和開發(fā)人員的移動應(yīng)用推薦系統(tǒng)并實現(xiàn)了系統(tǒng)原型。對用戶進(jìn)行移動應(yīng)用的個性化推薦時,不僅使用個性化推薦算法迎合用戶的興趣偏好,而且根據(jù)用戶個人信息中的人口統(tǒng)計學(xué)特征,如年齡、性別等提供了不同的推薦策略;為方便開發(fā)人員在海量的移動應(yīng)用中尋找參考信息,系統(tǒng)為開發(fā)人員提供了應(yīng)用的用戶畫像、基于用戶特征的應(yīng)用查詢、移動應(yīng)用之間的關(guān)聯(lián)度等。最后從信息論的角度,看待移動應(yīng)用產(chǎn)生、生長、衰亡,進(jìn)行系統(tǒng)維護(hù)。
[Abstract]:In recent years, the scale and technology of the Internet, especially the mobile Internet, have developed rapidly. Smart mobile devices such as smartphones and tablets have been widely used, and the number of smartphone users has increased dramatically. As an important part of smart phone, mobile application has changed the user's life style, working style, learning style and so on, which plays an important role in the system design and development. The number of mobile applications in the mobile application market, such as Google Play Store App Store, has reached a million-level. The vast amount of mobile applications provide convenience to users and provide reference to developers, but also bring new challenges. From the user's point of view, it is difficult to find the application that is interested in the mass application; from the developer's point of view, it takes a lot of energy to select the appropriate application as the reference in the mass application. At the same time, the younger age of smartphone users is a trend that can not be ignored, and how to prevent young users from indulging in smartphones is an urgent problem to be solved. In this paper, personalized recommendation for mobile applications is studied. Firstly, the recommendation algorithm is improved, and a resource diffusion algorithm based on user splitting is proposed. In the process of investigating the personalized recommendation algorithm, it is found that the interest offset accumulated by the user in the time dimension has a great influence on the accuracy of the recommendation algorithm. On the basis of the resource diffusion algorithm based on graph model, the time context information is incorporated into the algorithm, and a resource diffusion algorithm based on user splitting is proposed, which improves the traditional resource diffusion algorithm. The improved algorithm regards users whose interests change with time as multiple users, that is, using the idea of user splitting to introduce time context information, which makes full use of the data information. Compared with the traditional resource diffusion algorithm, the accuracy of the improved algorithm is obviously improved. Then, the resource diffusion algorithm based on user splitting is applied to mobile application recommendation field, and a mobile application recommendation system for smart phone users and developers is designed and implemented. In the process of personalized recommendation for mobile applications, not only the personalized recommendation algorithm is used to cater to the interests and preferences of users, but also different recommendation strategies are provided according to the demographic characteristics of users' personal information, such as age, gender, etc. In order to facilitate developers to find reference information in a large number of mobile applications, the system provides developers with user portraits, application queries based on user characteristics, and the correlation between mobile applications, etc. Finally, from the point of view of information theory, the generation, growth, decline and system maintenance of mobile applications are discussed.
【學(xué)位授予單位】：山東農(nóng)業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3

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