本文選題：App推薦算法　切入點(diǎn)：權(quán)限隱私　出處：《武漢大學(xué)》2017年碩士論文

【摘要】：近幾年隨著科學(xué)技術(shù)的飛躍發(fā)展,智能手機(jī)的普及使手機(jī)App呈現(xiàn)指數(shù)級(jí)增長,在紛繁的App中如何幫助用戶選擇感興趣的應(yīng)用已成為推薦系統(tǒng)中的熱門話題。在有關(guān)App推薦的已有系統(tǒng)中,大部分基于熱門度、下載使用量進(jìn)行推薦,因此極大程度推薦了非用戶所感興趣的App,同時(shí)越來越多的用戶開始關(guān)注涉及個(gè)人安全隱私的App權(quán)限。目前結(jié)合App權(quán)限、功能和用戶興趣三方面的推薦系統(tǒng)較少,大部分推薦系統(tǒng)只考慮權(quán)限隱私而沒有考慮用戶興趣做Top-N推薦,或者只將App權(quán)限隱私、功能和用戶興趣進(jìn)行簡單的線性組合做Top-N推薦,均隔離了App權(quán)限和用戶興趣之間的關(guān)聯(lián)性。本文提出了基于App權(quán)限、功能和用戶興趣相結(jié)合的矩陣分解算法(MFPF),利用兩者關(guān)聯(lián)性進(jìn)行App個(gè)性化推薦。本文工作的主要貢獻(xiàn)有以下幾方面:(1)結(jié)合本文實(shí)驗(yàn)數(shù)據(jù)進(jìn)行App權(quán)限分析,分析了普通App所需權(quán)限的數(shù)量、權(quán)限種類,以及普通App與惡意App所需權(quán)限的情況。(2)分析App權(quán)限隱私與用戶評(píng)分的關(guān)聯(lián)性,通過App-permission二分圖,建立ARSM方法量化App的危險(xiǎn)分值,并初步驗(yàn)證權(quán)限隱私和用戶興趣之間存在的關(guān)聯(lián)性。(3)建立兼具權(quán)限隱私和用戶興趣的App推薦模型,提出一種新穎的基于權(quán)限隱私和功能興趣的矩陣分解算法MFPF,通過結(jié)合App權(quán)限面、App功能屬性面及用戶興趣面實(shí)現(xiàn)App推薦。(4)分析權(quán)限在App用戶評(píng)分中的比重及影響,進(jìn)一步分析權(quán)限隱私與用戶評(píng)分的關(guān)聯(lián)性,即證明權(quán)限隱私和用戶興趣之間的關(guān)聯(lián)性。本文實(shí)驗(yàn)數(shù)據(jù)由安智市場的App和相應(yīng)用戶評(píng)分組成。實(shí)驗(yàn)結(jié)果表明,與傳統(tǒng)的推薦方法相比,本文提出的推薦算法推薦效果更好;與最新一篇同樣采用權(quán)限和功能的App推薦系統(tǒng)相比,本文算法的精確度更高。
[Abstract]:In recent years, with the rapid development of science and technology, the popularity of smart phones has led to an exponential increase in mobile phone App. How to help users choose interesting applications in the numerous App has become a hot topic in the recommendation system. In the existing system of App recommendation, most of them are based on the popularity of download usage. As a result, more and more users begin to pay attention to the App rights related to personal security and privacy. At present, there are fewer recommendation systems combining App permissions, functions and user interests. Most recommendation systems only consider privilege privacy without considering user interest to make Top-N recommendation, or simply linearly combine App privilege privacy, function and user interest to make Top-N recommendation. The relationship between App permissions and user interests is isolated. This paper proposes a new method based on App permissions. The matrix decomposition algorithm which combines function and user's interest makes use of the correlation between the two to carry out App personalized recommendation. The main contributions of this paper are as follows: 1) combining with the experimental data of this paper, the App privilege analysis is carried out. This paper analyzes the number and type of permission required by ordinary App, and the situation of common App and malicious App.) it analyzes the relationship between the privacy of App permission and the score of users, and establishes the ARSM method to quantify the risk score of App by App-permission dichotomy. And preliminarily verify the relationship between privacy and user interest. 3) establish a App recommendation model with both privilege privacy and user interest. This paper presents a novel matrix decomposition algorithm based on privilege privacy and functional interest. It analyzes the weight and influence of the privilege in the App users' score by combining the App function attribute surface and the user's interest surface to realize the App recommendation. The relationship between privilege privacy and user rating is further analyzed, that is, the correlation between authority privacy and user interest. The experimental data in this paper is composed of the App of Anzhi Market and the corresponding user score. The experimental results show that, Compared with the traditional recommendation method, the recommendation algorithm proposed in this paper is more effective, and compared with the latest App recommendation system, which also uses authority and function, the accuracy of this algorithm is higher.
【學(xué)位授予單位】：武漢大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3;TP311.56

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