【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(yán)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(zhǔn)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(xiàng)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(jì)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(shí)用戶誤判問題,由于真實(shí)用戶誤判會影響推薦系統(tǒng)的推薦精準(zhǔn)度,所以提出了一種基于嫌疑用戶聚類和目標(biāo)項(xiàng)目識別的攻擊用戶識別方法,在嫌疑攻擊類中進(jìn)一步準(zhǔn)確識別并標(biāo)記攻擊用戶。該算法首先識別目標(biāo)項(xiàng)目,然后在嫌疑攻擊類中識別并標(biāo)記攻擊用戶,目的是減小真實(shí)用戶誤判率,保障推薦系統(tǒng)的推薦精準(zhǔn)度。然后,針對推薦算法低魯棒性的問題,將攻擊用戶識別標(biāo)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(biāo)記的攻擊用戶對目標(biāo)項(xiàng)目的評分,目的是在保證推薦精準(zhǔn)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(shí)驗(yàn)平臺上模擬仿真實(shí)驗(yàn),并與一些經(jīng)典魯棒推薦算法作對比分析。實(shí)驗(yàn)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(zhǔn)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

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