本文關(guān)鍵詞： 最佳路徑 路徑信任 興趣相似度 協(xié)同過濾 預(yù)測(cè)評(píng)分　出處：《廣東技術(shù)師范學(xué)院》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：不斷發(fā)展的信息技術(shù)在帶給人們豐富網(wǎng)絡(luò)資源的同時(shí)也使人們陷入信息過載的困境,如何幫助用戶在海量數(shù)據(jù)中快速找到有價(jià)值的相關(guān)信息,是推薦技術(shù)研究的核心問題。協(xié)同過濾推薦算法是在眾多推薦技術(shù)中使用最為廣泛,但隨著在線社會(huì)網(wǎng)絡(luò)的不斷發(fā)展和社會(huì)網(wǎng)絡(luò)日益復(fù)雜,用戶間信任關(guān)系在一定程度上影響著推薦結(jié)果。傳統(tǒng)的協(xié)同過濾推薦方法主要存在以下問題:(1)數(shù)據(jù)稀疏性。用戶的評(píng)分?jǐn)?shù)據(jù)稀疏,使計(jì)算的相似度不準(zhǔn)確,導(dǎo)致評(píng)分預(yù)測(cè)不準(zhǔn)確,用戶無法得到合適的推薦信息。(2)易受攻擊性。開放的推薦系統(tǒng)允許用戶自由發(fā)布評(píng)分或評(píng)論,可能有些用戶提供虛假信息,導(dǎo)致推薦結(jié)果產(chǎn)生嚴(yán)重偏差,無法向用戶提供滿意推薦。(3)沒有考慮信任關(guān)系。傳統(tǒng)算法只是考慮了用戶的評(píng)分?jǐn)?shù)據(jù),未考慮用戶間可能存在的信任關(guān)系以及這種信任關(guān)系對(duì)推薦系統(tǒng)的價(jià)值。針對(duì)協(xié)同過濾推薦算法中存在的上述問題與挑戰(zhàn),本文提出了一種改進(jìn)的協(xié)同過濾算法方案,主要研究工作包括:(1)針對(duì)協(xié)同過濾推薦系統(tǒng)的數(shù)據(jù)稀疏問題,采用計(jì)算用戶間興趣相似度作為判斷用戶間相似度的一個(gè)依據(jù),區(qū)別于傳統(tǒng)算法只考慮用戶對(duì)項(xiàng)目的評(píng)分。(2)針對(duì)已有算法的易受攻擊問題,在推薦過程中,綜合考慮用戶間信任關(guān)系與興趣相似度以計(jì)算用戶間綜合相似度,從而緩解因用戶虛假評(píng)分導(dǎo)致的推薦結(jié)果不準(zhǔn)確問題。(3)提出一種基于最佳信任路徑的協(xié)同過濾推薦算法。用最佳信任路徑代替原算法的多路徑取平均值方法,在充分考慮信任路徑中其他用戶威望值的基礎(chǔ)上,選擇多條信任路徑中的最佳信任路徑,改善了原算法只考慮最終用戶威望值進(jìn)而缺乏客觀性的問題。實(shí)驗(yàn)結(jié)果表明,與基于用戶的協(xié)同過濾推薦算法和融合信任的協(xié)同過濾推薦算法相比,本文算法具有以下優(yōu)勢(shì):(1)推薦準(zhǔn)確度更高;(2)運(yùn)行效率更高,本文算法運(yùn)行時(shí)間是融合信任推薦算法運(yùn)行時(shí)間的四分之一,當(dāng)信任路徑增加時(shí),本文算法的優(yōu)勢(shì)更加明顯。
[Abstract]:The continuous development of information technology not only brings people rich network resources, but also makes people into the plight of information overload, how to help users quickly find valuable relevant information in the massive data. Collaborative filtering recommendation algorithm is the most widely used recommendation technology, but with the continuous development of online social network and the increasing complexity of social network. Trust relationship between users affects the recommendation results to some extent. The traditional collaborative filtering recommendation methods mainly have the following problems: 1) data sparsity. Because the similarity of calculation is not accurate, the score prediction is not accurate, and the user can not get the appropriate recommendation information. The open recommendation system allows the user to publish the rating or comment freely. It is possible that some users provide false information, which leads to serious deviation of recommendation results, which can not provide satisfactory recommendation to users. Trust relationship is not considered. The traditional algorithm only takes into account the users' rating data. This paper does not take into account the possible trust relationship between users and the value of this trust relationship to the recommendation system. Aiming at the above problems and challenges in collaborative filtering recommendation algorithm. In this paper, an improved collaborative filtering algorithm is proposed. The main research work includes: 1) data sparsity in collaborative filtering recommendation system. The interest similarity between users is calculated as a basis for judging the similarity between users, which is different from the traditional algorithm, which only considers the user's score on the items.) it is aimed at the vulnerability of the existing algorithms. In the process of recommendation, the trust relationship and interest similarity between users are considered comprehensively to calculate the comprehensive similarity between users. This alleviates the problem of inaccurate recommendation results caused by users' false ratings. This paper presents a collaborative filtering recommendation algorithm based on the best trust path, and uses the best trust path instead of the original algorithm to average the multi-path. On the basis of fully considering the other user prestige values in the trust path, we choose the best trust path in multiple trust paths. The original algorithm only considers the value of end-user prestige and lacks objectivity. The experimental results show that compared with the user-based collaborative filtering recommendation algorithm and the fusion trust collaborative filtering recommendation algorithm. This algorithm has the following advantages: 1) recommendation accuracy is higher; The running time of the proposed algorithm is 1/4 of that of the fusion trust recommendation algorithm. When the trust path is increased, the advantages of the proposed algorithm are more obvious.
【學(xué)位授予單位】：廣東技術(shù)師范學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 杜永萍;黃亮;何明;;融合信任計(jì)算的協(xié)同過濾推薦方法[J];模式識(shí)別與人工智能;2014年05期

2 奉國(guó)和;梁曉婷;;協(xié)同過濾推薦研究綜述[J];圖書情報(bào)工作;2011年16期

3 蔡浩;賈宇波;黃成偉;;結(jié)合用戶信任模型的協(xié)同過濾推薦方法研究[J];計(jì)算機(jī)工程與應(yīng)用;2010年35期

4 金亞亞;牟援朝;;基于改進(jìn)信任度的協(xié)同過濾推薦算法[J];現(xiàn)代圖書情報(bào)技術(shù);2010年10期

5 劉建國(guó);周濤;郭強(qiáng);汪秉宏;;個(gè)性化推薦系統(tǒng)評(píng)價(jià)方法綜述[J];復(fù)雜系統(tǒng)與復(fù)雜性科學(xué);2009年03期

6 許海玲;吳瀟;李曉東;閻保平;;互聯(lián)網(wǎng)推薦系統(tǒng)比較研究[J];軟件學(xué)報(bào);2009年02期

7 郭艷紅;鄧貴仕;雒春雨;;基于信任因子的協(xié)同過濾推薦算法[J];計(jì)算機(jī)工程;2008年20期

8 李聰;梁昌勇;馬麗;;基于領(lǐng)域最近鄰的協(xié)同過濾推薦算法[J];計(jì)算機(jī)研究與發(fā)展;2008年09期

9 鄧愛林,朱揚(yáng)勇,施伯樂;基于項(xiàng)目評(píng)分預(yù)測(cè)的協(xié)同過濾推薦算法[J];軟件學(xué)報(bào);2003年09期

10 徐小琳,闕喜戎,程時(shí)端;信息過濾技術(shù)和個(gè)性化信息服務(wù)[J];計(jì)算機(jī)工程與應(yīng)用;2003年09期

相關(guān)博士學(xué)位論文 前1條

1 鄧愛林;電子商務(wù)推薦系統(tǒng)關(guān)鍵技術(shù)研究[D];復(fù)旦大學(xué);2003年

相關(guān)碩士學(xué)位論文 前1條

1 馮萍萍;融合雙重信任網(wǎng)絡(luò)的協(xié)同過濾推薦算法研究[D];燕山大學(xué);2014年

，

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