【摘要】：隨著大數(shù)據(jù)時代的到來和電子商務(wù)的發(fā)展,協(xié)同過濾推薦系統(tǒng)以其個性化的推薦優(yōu)勢正逐漸滲透人們的生活。但是由于推薦系統(tǒng)的開放性,容易招致惡意用戶向推薦系統(tǒng)中注入攻擊概貌以改變推薦結(jié)果,這嚴重影響了推薦系統(tǒng)的安全性。本文針對這個問題提出了一種魯棒推薦算法,用于提高推薦系統(tǒng)的魯棒性,同時保證推薦精準度。具體研究內(nèi)容如下。首先,針對推薦系統(tǒng)中惡意攻擊用戶的存在影響推薦系統(tǒng)魯棒性的問題,提出了一種嫌疑用戶聚類算法,該算法從項目流行度入手,引入了用戶平均評分流行度的概念,并基于此概念重新定義了用戶之間距離的計算公式。該算法目的是將嫌疑用戶聚集到一類,方便攻擊用戶的識別。其次,針對嫌疑用戶聚類算法中存在的真實用戶誤判問題,由于真實用戶誤判會影響推薦系統(tǒng)的推薦精準度,所以提出了一種基于嫌疑用戶聚類和目標項目識別的攻擊用戶識別方法,在嫌疑攻擊類中進一步準確識別并標記攻擊用戶。該算法首先識別目標項目,然后在嫌疑攻擊類中識別并標記攻擊用戶,目的是減小真實用戶誤判率,保障推薦系統(tǒng)的推薦精準度。然后,針對推薦算法低魯棒性的問題,將攻擊用戶識別標記結(jié)果與貝葉斯概率矩陣分解模型結(jié)合,形成基于攻擊用戶識別和貝葉斯概率矩陣分解的魯棒推薦算法。該算法在模型的學(xué)習(xí)過程中屏蔽被標記的攻擊用戶對目標項目的評分,目的是在保證推薦精準度的同時提高推薦系統(tǒng)的魯棒性。最后利用MovieLens 100K數(shù)據(jù)集在Mat Lab實驗平臺上模擬仿真實驗,并與一些經(jīng)典魯棒推薦算法作對比分析。實驗結(jié)果表明,本文算法可以在提高推薦魯棒性的同時保證推薦精準度。
[Abstract]:With the arrival of big data era and the development of electronic commerce, collaborative filtering recommendation system is gradually infiltrating people's life with its personalized recommendation advantage. However, due to the openness of the recommendation system, it is easy to cause malicious users to inject an attack profile into the recommendation system to change the recommendation result, which seriously affects the security of the recommendation system. In this paper, a robust recommendation algorithm is proposed to improve the robustness of recommendation system and ensure the accuracy of recommendation. The specific contents of the study are as follows. First of all, aiming at the problem that malicious attacks on users in recommendation system affect the robustness of recommendation system, a clustering algorithm for suspected users is proposed. The concept of average user rating popularity is introduced in this algorithm based on item popularity. Based on this concept, the formula for calculating the distance between users is redefined. The purpose of this algorithm is to cluster suspected users into a class and to facilitate the identification of attacking users. Secondly, aiming at the real user misjudgment problem in the suspect user clustering algorithm, the recommendation accuracy of the recommendation system will be affected by the real user misjudgment. Therefore, an attack user identification method based on suspect user clustering and target item identification is proposed, which can further accurately identify and mark the attack user in the suspect attack class. The algorithm first identifies the target item and then identifies and marks the target user in the suspect attack class. The purpose of the algorithm is to reduce the false judgment rate of the real user and to ensure the recommendation accuracy of the recommendation system. Then, aiming at the problem of low robustness of recommendation algorithm, a robust recommendation algorithm based on attack user identification and Bayesian probability matrix decomposition is formed by combining the result of attack user identification with Bayesian probability matrix decomposition model. In order to improve the robustness of the recommendation system, the algorithm blocks the target item score of the target user during the learning process of the model. The goal of the algorithm is to ensure the accuracy of the recommendation and improve the robustness of the recommendation system. Finally, the MovieLens 100K data set is used to simulate the simulation experiment on the Mat Lab platform, and it is compared with some classical robust recommendation algorithms. The experimental results show that the proposed algorithm can improve the robustness of recommendation and ensure the accuracy of recommendation.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.3

【相似文獻】

相關(guān)期刊論文 前10條

1 徐義峰;徐云青;劉曉平;;一種基于時間序列性的推薦算法[J];計算機系統(tǒng)應(yīng)用;2006年10期

2 余小鵬;;一種基于多層關(guān)聯(lián)規(guī)則的推薦算法研究[J];計算機應(yīng)用;2007年06期

3 張海玉;劉志都;楊彩;賈松浩;;基于頁面聚類的推薦算法的改進[J];計算機應(yīng)用與軟件;2008年09期

4 張立燕;;一種基于用戶事務(wù)模式的推薦算法[J];福建電腦;2009年03期

5 王晗;夏自謙;;基于蟻群算法和瀏覽路徑的推薦算法研究[J];中國科技信息;2009年07期

6 周珊丹;周興社;王海鵬;倪紅波;張桂英;苗強;;智能博物館環(huán)境下的個性化推薦算法[J];計算機工程與應(yīng)用;2010年19期

7 王文;;個性化推薦算法研究[J];電腦知識與技術(shù);2010年16期

8 張愷;秦亮曦;寧朝波;李文閣;;改進評價估計的混合推薦算法研究[J];微計算機信息;2010年36期

9 夏秀峰;代沁;叢麗暉;;用戶顯意識下的多重態(tài)度個性化推薦算法[J];計算機工程與應(yīng)用;2011年16期

10 楊博;趙鵬飛;;推薦算法綜述[J];山西大學(xué)學(xué)報(自然科學(xué)版);2011年03期

相關(guān)會議論文 前10條

1 王韜丞;羅喜軍;杜小勇;;基于層次的推薦:一種新的個性化推薦算法[A];第二十四屆中國數(shù)據(jù)庫學(xué)術(shù)會議論文集（技術(shù)報告篇）[C];2007年

2 唐燦;;基于模糊用戶心理模式的個性化推薦算法[A];2008年計算機應(yīng)用技術(shù)交流會論文集[C];2008年

3 秦國;杜小勇;;基于用戶層次信息的協(xié)同推薦算法[A];第二十一屆中國數(shù)據(jù)庫學(xué)術(shù)會議論文集（技術(shù)報告篇）[C];2004年

4 周玉妮;鄭會頌;;基于瀏覽路徑選擇的蟻群推薦算法:用于移動商務(wù)個性化推薦系統(tǒng)[A];社會經(jīng)濟發(fā)展轉(zhuǎn)型與系統(tǒng)工程——中國系統(tǒng)工程學(xué)會第17屆學(xué)術(shù)年會論文集[C];2012年

5 蘇日啟;胡皓;汪秉宏;;基于網(wǎng)絡(luò)的含時推薦算法[A];第五屆全國復(fù)雜網(wǎng)絡(luò)學(xué)術(shù)會議論文（摘要）匯集[C];2009年

6 梁莘q，

本文編號：2182890