本文關(guān)鍵詞： 個(gè)性化推薦 協(xié)同過濾 用戶屬性 興趣對(duì)比度　出處：《華中師范大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：為解決信息過載問題和應(yīng)對(duì)用戶對(duì)個(gè)性化服務(wù)的需求,個(gè)性化推薦技術(shù)應(yīng)運(yùn)而生,本文希望通過對(duì)個(gè)性化推薦的優(yōu)化與創(chuàng)新,讓用戶能夠更快更精準(zhǔn)的找到自己想要的資源。在眾多個(gè)性化推薦技術(shù)中,協(xié)同過濾算法是當(dāng)下研究的熱門。因?yàn)槠渌惴ǖ膽?yīng)用范圍最廣泛,發(fā)展時(shí)間最長(zhǎng),算法最成熟。協(xié)同過濾推薦主要是根據(jù)存貯在系統(tǒng)數(shù)據(jù)庫(kù)中用戶歷史消費(fèi)及評(píng)分?jǐn)?shù)據(jù),來分析用戶的興趣,預(yù)測(cè)用戶未來可能消費(fèi)什么樣的產(chǎn)品,從而對(duì)其實(shí)施個(gè)性化推薦。以往的協(xié)同過濾算法研究,主要是以用戶評(píng)分矩陣為基礎(chǔ),進(jìn)行用戶偏好的感知,以用戶打分的相似性來判斷用戶之間興趣的相似性。隨著算法的發(fā)展,特別是可擴(kuò)展性問題、冷啟動(dòng)問題等算法瓶頸的出現(xiàn),純粹依賴評(píng)分矩陣數(shù)據(jù)來尋找最近鄰,就顯得力不從心。因此,必須尋找其他有效的用戶偏好數(shù)據(jù)來源。本文對(duì)用戶偏好的感知方法進(jìn)行改進(jìn),引入了用戶屬性信息這一重要的偏好感知數(shù)據(jù)源,與評(píng)分矩陣共同構(gòu)成用戶偏好感知的數(shù)據(jù)基礎(chǔ)。用戶屬性作為描述用戶個(gè)體特征的重要信息,不同的屬性可以將用戶劃分到不同類別的群體當(dāng)中,這些用戶群可能存在一定的興趣偏好相似性,將這些特定用戶群的共同興趣找出來,作為產(chǎn)生推薦的基礎(chǔ)。本文定義了一個(gè)新的衡量用戶興趣偏好的參數(shù),即：興趣對(duì)比度。在此基礎(chǔ)上,提出了一個(gè)融合用戶屬性和興趣對(duì)比度的協(xié)同過濾個(gè)性化推薦算法,該算法以用戶屬性組合為約束,結(jié)合興趣對(duì)比度共同產(chǎn)生待推薦集合,經(jīng)過整理刪選后形成最后的推薦列表。本文提出的新算法,將克服傳統(tǒng)協(xié)同過濾的可擴(kuò)展性問題作為改進(jìn)的目標(biāo)。在新算法的整個(gè)流程設(shè)計(jì)中,不依賴傳統(tǒng)的用戶相似性計(jì)算來尋找最近鄰。因此,當(dāng)用戶和項(xiàng)目快速增長(zhǎng)時(shí),不會(huì)出現(xiàn)算法復(fù)雜度急劇上升的情況,實(shí)驗(yàn)證明,融合用戶屬性和興趣對(duì)比度的協(xié)同過濾推薦算法,能夠在保證推薦實(shí)時(shí)性的前提下,達(dá)到滿意的推薦質(zhì)量,是一種靈活高效的推薦方案,更重要是提供了一種新的推薦思路。此外,本文還對(duì)不同屬性組合下的推薦效率進(jìn)行了系統(tǒng)分析,為該領(lǐng)域的相關(guān)研究奠定了一定的基礎(chǔ)。
[Abstract]:In order to solve the problem of information overload and to meet the needs of users for personalized service, personalized recommendation technology emerges as the times require. This paper hopes to optimize and innovate personalized recommendation. Among the many personalized recommendation technologies, collaborative filtering algorithm is a hot research topic, because it has the most extensive application and the longest development time. The most mature algorithm is to analyze the interests of users and predict what kind of products they may consume in the future according to the historical consumption and scoring data stored in the system database. In order to implement personalized recommendation, the previous collaborative filtering algorithms are mainly based on the user score matrix, the perception of user preferences, With the development of the algorithm, especially the problem of scalability, cold start problem and other bottlenecks, we rely solely on the score matrix data to find the nearest neighbor. Therefore, we must find other effective sources of user preference data. In this paper, we improve the perception method of user preference and introduce user attribute information as an important data source of preference perception. Together with the score matrix, it forms the data base of user preference perception. As an important information describing the individual characteristics of users, different attributes can divide users into different groups. These user groups may have a certain similarity of interest preferences. The common interests of these specific user groups can be found as the basis for producing recommendations. In this paper, a new parameter to measure user interest preference is defined. That is: interest contrast. On this basis, a collaborative filtering personalized recommendation algorithm combining user attributes and interest contrast is proposed. After sorting and deleting, the final recommendation list is formed. The new algorithm proposed in this paper aims to overcome the scalability problem of traditional collaborative filtering. In the whole process design of the new algorithm, We do not rely on the traditional user similarity calculation to find the nearest neighbor. Therefore, when the user and project grow rapidly, the algorithm complexity will not rise sharply. The collaborative filtering recommendation algorithm which combines user attributes and interest contrast can achieve satisfactory recommendation quality on the premise of ensuring real-time recommendation. It is a flexible and efficient recommendation scheme. More importantly, it provides a new way of recommendation. In addition, this paper also makes a systematic analysis of the efficiency of recommendation under different attribute combinations, which lays a foundation for the related research in this field.
【學(xué)位授予單位】：華中師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F224;F713.36

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 印桂生;崔曉暉;馬志強(qiáng);;遺忘曲線的協(xié)同過濾推薦模型[J];哈爾濱工程大學(xué)學(xué)報(bào);2012年01期

2 龍舜;蔡跳;林佳雄;;一個(gè)基于演化關(guān)聯(lián)規(guī)則挖掘的個(gè)性化推薦模型[J];暨南大學(xué)學(xué)報(bào)(自然科學(xué)與醫(yī)學(xué)版);2012年03期

3 郭偉光;李道芳;章蕾;;一種社會(huì)化標(biāo)注系統(tǒng)資源個(gè)性化推薦方法[J];計(jì)算機(jī)工程與應(yīng)用;2011年10期

4 李改;李磊;;基于矩陣分解的協(xié)同過濾算法[J];計(jì)算機(jī)工程與應(yīng)用;2011年30期

5 王茜;王均波;;一種改進(jìn)的協(xié)同過濾推薦算法[J];計(jì)算機(jī)科學(xué);2010年06期

6 郭艷紅;鄧貴仕;;協(xié)同過濾系統(tǒng)項(xiàng)目冷啟動(dòng)的混合推薦算法[J];計(jì)算機(jī)工程;2008年23期

7 李闖;楊勝;李仁發(fā);;一個(gè)最優(yōu)分類關(guān)聯(lián)規(guī)則算法[J];計(jì)算機(jī)工程與科學(xué);2009年04期

8 王玉祥;喬秀全;李曉峰;孟洛明;;上下文感知的移動(dòng)社交網(wǎng)絡(luò)服務(wù)選擇機(jī)制研究[J];計(jì)算機(jī)學(xué)報(bào);2010年11期

9 謝意;陳德人;干紅華;;基于瀏覽偏好挖掘的實(shí)時(shí)商品推薦方法[J];計(jì)算機(jī)應(yīng)用;2011年01期

10 王潔;湯小春;;基于社區(qū)網(wǎng)絡(luò)內(nèi)容的個(gè)性化推薦算法研究[J];計(jì)算機(jī)應(yīng)用研究;2011年04期

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