本文選題：推薦系統(tǒng) + 攻擊檢測(cè)�。� 參考：《燕山大學(xué)》2016年碩士論文

【摘要】：信息時(shí)代的到來,方便了我們的生活,拓展了我們的眼界。與此同時(shí)出現(xiàn)的問題是,信息的過載給人們帶來麻煩,想要快速找到所需的信息所付出的代價(jià)越來越高。搜索引擎和協(xié)同過濾技術(shù)是當(dāng)前解決這一問題的兩種主流手段。值得一提的是協(xié)同過濾技術(shù)在個(gè)性化定制方面對(duì)用戶具有很強(qiáng)的吸引力。伴隨電子商務(wù)的發(fā)展,協(xié)同過濾技術(shù)正融入到其中,成為推薦系統(tǒng)的核心部分,推薦系統(tǒng)的出現(xiàn)大大提升了用戶的購(gòu)物、聽音樂等使用體驗(yàn)。然而由于推薦系統(tǒng)本身的開放性,使得其容易遭受惡意用戶的攻擊,直接影響到了用戶的使用體驗(yàn),間接影響了電子商務(wù)的生存。本文首先分析了目前對(duì)于這一問題研究的主要解決技術(shù)和國(guó)內(nèi)外現(xiàn)狀,同時(shí)對(duì)于托攻擊、托攻擊模型、托攻擊特征等進(jìn)行了詳細(xì)的描述。針對(duì)托攻擊通過協(xié)同作用影響推薦系統(tǒng)這一問題,本文主要圍繞托攻擊的特點(diǎn)以及作用的方式來進(jìn)行了思考與研究。對(duì)于識(shí)別單個(gè)用戶概貌的托攻擊檢測(cè)算法,推薦系統(tǒng)中的“專家型”用戶往往被錯(cuò)誤標(biāo)記,它們所展現(xiàn)的“與眾不同”的特征與虛假概貌會(huì)很相似。首先,基于信號(hào)理論當(dāng)中的去噪原理,本文基于主成分分析方法對(duì)其進(jìn)行了改進(jìn),結(jié)合了邏輯斯蒂回歸進(jìn)行有監(jiān)督分類,該算法能夠有效地去除對(duì)攻擊強(qiáng)度這一先驗(yàn)知識(shí)的依賴,并且在準(zhǔn)確率這一評(píng)價(jià)指標(biāo)上有較好的表現(xiàn),該算法具有較好的實(shí)際應(yīng)用價(jià)值。然后,對(duì)于多種混合攻擊類型,先前所提出的算法效果較差。對(duì)于此問題,本文提出了結(jié)合信息熵和主題模型的托攻擊檢測(cè)算法,使用主題模型得到用戶的主題分布,托攻擊概貌的主題集中,即對(duì)應(yīng)的信息熵較小;相反地,正常用戶的通常含有多個(gè)主題,即對(duì)應(yīng)的信息熵較大。最后,對(duì)前面所提出的算法進(jìn)行了實(shí)驗(yàn)驗(yàn)證,將兩個(gè)算法在兩個(gè)不同的數(shù)據(jù)集上進(jìn)行對(duì)比實(shí)驗(yàn)和結(jié)果分析。結(jié)果表明,本文提出的改進(jìn)后的算法相較于原始算法,大大提高了預(yù)測(cè)的準(zhǔn)確率。
[Abstract]:The arrival of the information age has facilitated our lives and broadened our horizons. At the same time, the problem is that the overload of information brings trouble to people, and the cost of finding the information quickly is becoming higher and higher. Search engine and collaborative filtering technology are two main methods to solve this problem. It is worth mentioning that collaborative filtering technology has a strong appeal to users in personalized customization. With the development of electronic commerce, collaborative filtering technology is becoming the core part of the recommendation system. The appearance of the recommendation system greatly improves the user's experience of shopping, listening to music and so on. However, because of the openness of recommendation system, it is vulnerable to malicious user attacks, which directly affects the user's experience and indirectly affects the survival of e-commerce. This paper first analyzes the main research technologies and the current situation at home and abroad for this problem, and at the same time, describes in detail the supporting attack, the model of the supporting attack, the characteristics of the supporting attack, and so on. Aiming at the problem that the depot attack affects the recommendation system through synergy, this paper mainly focuses on the characteristics of the depot attack and the way in which it works. For the trust attack detection algorithm for recognizing the profile of a single user, the "expert" users in the recommendation system are often wrongly marked, and their "distinctive" features will be very similar to the false profile. Firstly, based on the principle of de-noising in signal theory, this paper improves it based on principal component analysis (PCA). The algorithm can effectively remove the dependence on the prior knowledge of attack intensity and has a good performance on the evaluation index of accuracy. The algorithm has good practical application value. Then, for various types of mixed attacks, the proposed algorithm has a poor effect. In order to solve this problem, this paper proposes an algorithm combining information entropy and topic model, which can get the user's topic distribution, the theme set of the general profile of the support attack, that is, the corresponding information entropy is small; on the contrary, Normal users usually contain more than one topic, that is, the corresponding information entropy is larger. Finally, the proposed algorithm is verified by experiments, and the two algorithms are compared with each other on two different data sets. The results show that the improved algorithm greatly improves the prediction accuracy compared with the original algorithm.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.3

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