本文選題：波動因子 + 用戶可信任度　； 參考：《重慶大學(xué)》2014年碩士論文

【摘要】：協(xié)同過濾是目前個性化推薦系統(tǒng)中應(yīng)用最為普遍和成熟的技術(shù)。協(xié)同過濾技術(shù)通過分析用戶的歷史行為記錄獲取用戶之間或者項目之間的相互關(guān)系，然后通過該關(guān)系為各個用戶或者項目找到他們的最近鄰居，然后使用這些最近鄰居為目標(biāo)用戶推薦相關(guān)的項目以達到挖掘用戶潛在興趣的目的。但是該技術(shù)仍然存在著數(shù)據(jù)稀疏性、冷啟動、可擴展性和系統(tǒng)脆弱性等問題。本文主要針對數(shù)據(jù)稀缺性問題以及系統(tǒng)脆弱性問題進行深入研究，并據(jù)此改進傳統(tǒng)協(xié)同過濾算法，使其在數(shù)據(jù)稀疏的情況下具有更高的推薦精度，并且能夠?qū)Ω鞣N常見的攻擊有較好的抵抗能力。 首先，本文指出通常只有那些在計算相似度的時候需要使用缺失數(shù)據(jù)的算法才會直接面臨數(shù)據(jù)稀缺性問題。由于皮爾遜相關(guān)相似度的計算并不使用缺失數(shù)據(jù)，所以在計算皮爾遜相關(guān)相似度的時候不需要直接面臨數(shù)據(jù)稀缺的問題，而是面臨著共同評分項數(shù)目不同的問題（即“波動因子”問題）。本文在引出波動因子的問題后，系統(tǒng)分析了相似度在不同波動因子影響下的分布情況，并根據(jù)該分布情況提出一種簡單有效的方法消除波動因子對相似度計算的影響。接著，使用消除波動因子影響的協(xié)同過濾算法(包括user-based以及item-based協(xié)同過濾算法)在Movielens的數(shù)據(jù)集上進行實驗，，實驗表明消除波動因子影響的協(xié)同過濾算法在推薦精度上較原始算法有較大的提高。 然后，針對協(xié)同過濾算法易受到攻擊的問題，本文從用戶可信任度的角度出發(fā)，提出一種簡單的基于統(tǒng)計的方法來計算各用戶的可信任度，并根據(jù)用戶可信任度加強協(xié)同過濾算法的抗攻擊能力。 接著，本文將改進的消除波動因子影響的相似度計算算法應(yīng)用到傳統(tǒng)協(xié)同過濾算法中，并且結(jié)合用戶可信任度提出了一種抗攻擊的協(xié)同過濾算法。為了驗證改進后算法的推薦能力以及抗攻擊能力，在Movielens數(shù)據(jù)集上進行實驗，實驗表明改進后的算法在推薦精度以及抗攻擊能力上都比原始算法有較大的改進。 最后，將本文的研究內(nèi)容與“第四方就業(yè)信息平臺”項目相結(jié)合進行相關(guān)的應(yīng)用研究。
[Abstract]:Collaborative filtering is the most popular and mature technology in personalized recommendation system. Collaborative filtering obtains the relationships between users or projects by analyzing their historical behavior records, and then finds their nearest neighbors for each user or project. Then, these nearest neighbors are used to recommend related items for the target users for the purpose of mining the potential interests of the users. However, there are still some problems such as data sparsity, cold start, extensibility and system vulnerability. In this paper, the data scarcity problem and the system vulnerability problem are studied in depth, and the traditional collaborative filtering algorithm is improved to make it have higher recommendation accuracy when the data is sparse. And it can resist all kinds of common attacks. Firstly, this paper points out that only those algorithms that need to use missing data to calculate similarity will face the problem of data scarcity directly. Because Pearson correlation similarity does not use missing data, there is no need to directly face the problem of data scarcity in computing Pearson correlation similarity. Rather, they are faced with the problem of different numbers of common scores (i.e., "volatility factor"). After introducing the problem of fluctuation factor, this paper systematically analyzes the distribution of similarity under the influence of different fluctuation factors, and puts forward a simple and effective method to eliminate the influence of fluctuation factor on similarity calculation. Then, the co-filtering algorithm (including user-based and item-based co-filtering algorithm) which eliminates the influence of fluctuation factor is used to carry out experiments on Movielens data set. The experiment shows that the collaborative filtering algorithm which eliminates the influence of fluctuation factor has higher recommendation accuracy than the original one. Then, aiming at the problem that collaborative filtering algorithm is vulnerable to attack, this paper starts from the perspective of user trust. A simple statistical method is proposed to calculate the trust degree of each user, and the anti-attack ability of the collaborative filtering algorithm is enhanced according to the trust degree of the user. In this paper, we apply the improved similarity calculation algorithm to the traditional collaborative filtering algorithm, and propose an anti-attack collaborative filtering algorithm combined with the user's trustworthiness. In order to verify the recommendation ability and anti-attack ability of the improved algorithm, the experiment on Movielens dataset shows that the improved algorithm has better recommendation accuracy and anti-attack ability than the original algorithm. The research content of this paper is combined with the fourth party employment information platform project to carry on the related application research.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP391.3;TP393.08

【參考文獻】

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本文編號：2000014