發(fā)布時(shí)間：2018-04-23 10:11

  本文選題：協(xié)同過(guò)濾 + 魯棒推薦��； 參考：《燕山大學(xué)》2016年博士論文

【摘要】：協(xié)同過(guò)濾推薦系統(tǒng)被廣泛地應(yīng)用到電子商務(wù)網(wǎng)站等諸多領(lǐng)域,可以有效解決“信息超載”問(wèn)題。但是,一些惡意用戶(hù)蓄意偽造虛假用戶(hù)評(píng)分來(lái)干擾系統(tǒng)的決策推薦過(guò)程,企圖使系統(tǒng)產(chǎn)生有利于個(gè)人的推薦結(jié)果,這種惡意攻擊行為嚴(yán)重影響了系統(tǒng)的推薦質(zhì)量以及用戶(hù)對(duì)系統(tǒng)的信任。因此,如何保障推薦系統(tǒng)不受惡意攻擊的影響,為用戶(hù)提供真實(shí)可靠的推薦結(jié)果已經(jīng)成為一個(gè)值得研究的熱點(diǎn)問(wèn)題。本文基于可疑用戶(hù)度量的思想,從基于內(nèi)存和基于模型的推薦技術(shù)兩方面展開(kāi)研究,致力于設(shè)計(jì)一系列魯棒性高、精度損失少的協(xié)同過(guò)濾推薦算法。首先,針對(duì)基于用戶(hù)的推薦算法近鄰選取可靠性不高的問(wèn)題,提出一種基于k-距離和項(xiàng)目類(lèi)別信息的魯棒推薦方法。根據(jù)離群點(diǎn)檢測(cè)思想,實(shí)現(xiàn)用戶(hù)可疑度計(jì)算;將用戶(hù)可疑度與項(xiàng)目類(lèi)別信息相融合,給出缺失值填充計(jì)算方法,對(duì)用戶(hù)的未評(píng)分項(xiàng)進(jìn)行填充;基于填充后的評(píng)分矩陣,結(jié)合傳統(tǒng)的基于用戶(hù)的協(xié)同過(guò)濾推薦技術(shù)將用戶(hù)相似度和可疑度共同作為選取鄰居的依據(jù),實(shí)現(xiàn)對(duì)目標(biāo)用戶(hù)的魯棒推薦。其次,針對(duì)已有信任計(jì)算模型在攻擊概貌存在情況下對(duì)用戶(hù)間信任關(guān)系度量不準(zhǔn)確的問(wèn)題,提出一種基于可疑用戶(hù)度量和多維信任的魯棒推薦方法。根據(jù)用戶(hù)概貌的特征訓(xùn)練相關(guān)向量機(jī)分類(lèi)器,對(duì)用戶(hù)可疑度進(jìn)行度量;基于用戶(hù)評(píng)分信息挖掘用戶(hù)之間的隱式信任關(guān)系,結(jié)合用戶(hù)可疑性信息構(gòu)建可靠多維信任模型;將可靠多維信任模型與基于用戶(hù)的近鄰?fù)扑]模型相融合,完成對(duì)目標(biāo)用戶(hù)的可靠推薦。再次,針對(duì)基于矩陣分解的推薦算法在面對(duì)托攻擊時(shí)魯棒性較差的問(wèn)題,提出一種基于模糊核聚類(lèi)和支持向量機(jī)的魯棒推薦方法。根據(jù)攻擊概貌間高相似度的特性,利用模糊核聚類(lèi)技術(shù)在高維特征空間對(duì)用戶(hù)概貌進(jìn)行聚類(lèi),將攻擊概貌聚到同一類(lèi)內(nèi);利用支持向量機(jī)分類(lèi)器對(duì)含有攻擊概貌的聚類(lèi)進(jìn)行檢測(cè),進(jìn)一步識(shí)別攻擊概貌;將攻擊概貌識(shí)別結(jié)果融入到矩陣分解過(guò)程中,提高算法的魯棒性。然后,針對(duì)基于矩陣分解的推薦算法不能平衡處理魯棒性和推薦精度的問(wèn)題,提出一種基于可疑用戶(hù)識(shí)別和Tukey M-估計(jì)量的魯棒推薦方法。根據(jù)用戶(hù)評(píng)分信息的分布情況,提出評(píng)分個(gè)數(shù)偏離度和鄰居平均相似度的計(jì)算方法,對(duì)可疑用戶(hù)進(jìn)行識(shí)別,將識(shí)別結(jié)果與傳統(tǒng)的近鄰選取思想相結(jié)合,構(gòu)建可靠近鄰模型;在矩陣分解過(guò)程中引入Tukey M-估計(jì)量,構(gòu)造魯棒矩陣分解模型;將可靠近鄰模型融入到魯棒矩陣分解模型中,在提高算法魯棒性的同時(shí)提高推薦精度。最后,在MovieLens數(shù)據(jù)集上與現(xiàn)有的經(jīng)典方法進(jìn)行了實(shí)驗(yàn)對(duì)比分析,驗(yàn)證了所提方法的有效性。
[Abstract]:Collaborative filtering recommendation system is widely used in many fields, such as e-commerce websites, which can effectively solve the problem of "information overload". However, some malicious users deliberately falsify false user ratings to interfere with the decision-making and recommendation process of the system, in an attempt to make the system produce recommendations in the interests of individuals. This malicious attack seriously affects the recommendation quality of the system and user's trust in the system. Therefore, how to protect the recommendation system from malicious attacks and provide users with reliable recommendation results has become a hot issue worthy of study. Based on the idea of suspect user metrics, this paper studies the memory and model-based recommendation techniques, and designs a series of collaborative filtering recommendation algorithms with high robustness and low precision loss. Firstly, a robust recommendation method based on k- distance and item category information is proposed to solve the problem of low reliability of nearest neighbor selection based on user-based recommendation algorithm. According to the idea of outlier detection, the user suspect degree can be calculated; the missing value filling calculation method is given by combining the user suspicious degree with item category information; based on the filled score matrix, Combined with the traditional user-based collaborative filtering recommendation technology, the similarity and suspicious degree of users are taken as the basis for selecting neighbors, and the robust recommendation to target users is realized. Secondly, a robust recommendation method based on suspect user metrics and multidimensional trust is proposed to solve the problem of inaccurate measurement of trust relationships between users in the presence of existing trust computing models. According to the features of the user profile, the correlation vector machine classifier is trained to measure the degree of user suspicion, the implicit trust relationship between users is mined based on the user score information, and the reliable multi-dimensional trust model is constructed by combining the user suspicious information. The reliable multi-dimension trust model is combined with the user-based nearest neighbor recommendation model to complete the reliable recommendation to the target user. Thirdly a robust recommendation method based on fuzzy kernel clustering and support vector machine is proposed to solve the problem of poor robustness of the recommendation algorithm based on matrix decomposition. According to the characteristics of high similarity between attack profiles, fuzzy kernel clustering technology is used to cluster the user profile in high dimensional feature space, and the attack profile is clustered into the same class. Support vector machine (SVM) classifier is used to detect the cluster with attack profile, to further identify the attack profile, and to incorporate the result of attack profile recognition into matrix decomposition process, so as to improve the robustness of the algorithm. Then, a robust recommendation method based on suspect user identification and Tukey M- estimator is proposed to solve the problem that the recommendation algorithm based on matrix decomposition can not deal with the problem of robustness and recommendation accuracy. According to the distribution of the users' rating information, the method of calculating the number deviation of the score and the average similarity of the neighbors is put forward. The suspicious users are identified, and the identification results are combined with the traditional idea of nearest neighbor selection to construct the reliable nearest neighbor model. In the process of matrix decomposition, Tukey M- estimator is introduced to construct the robust matrix decomposition model, and the reliable nearest neighbor model is incorporated into the robust matrix decomposition model, which improves the robustness of the algorithm and improves the recommendation accuracy. Finally, the effectiveness of the proposed method is verified by comparing with the existing classical methods on the MovieLens dataset.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP391.3

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