【摘要】：互聯(lián)網(wǎng)的迅猛發(fā)展,給人們的生活方式帶來(lái)了強(qiáng)大的沖擊,豐富便捷的信息獲取方式引發(fā)了全球范圍內(nèi)的信息革命。在這樣的背景下,大多數(shù)商業(yè)網(wǎng)站建立起商品推薦系統(tǒng),為人們提供更加直觀有效的服務(wù),但是至今為止,推薦服務(wù)卻沒(méi)有在圖書(shū)館應(yīng)用方面得到足夠的重視。 本文以提高圖書(shū)管理中圖書(shū)推薦服務(wù)為目的,將數(shù)據(jù)挖掘技術(shù)引入到圖書(shū)館管理系統(tǒng)中,文章首先對(duì)比了國(guó)內(nèi)外圖書(shū)推薦系統(tǒng)研究狀況,指出圖書(shū)館信息推薦服務(wù)應(yīng)該分為幾個(gè)方面,需要哪些技術(shù)支持,并且介紹了常用的推薦技術(shù),對(duì)比它們的優(yōu)勢(shì)和不足,選擇適合進(jìn)行書(shū)目推薦的推薦技術(shù)。然后詳細(xì)的介紹進(jìn)行書(shū)目推薦的數(shù)據(jù)挖掘方法：聚類分析方法、關(guān)聯(lián)規(guī)則分析方法、決策樹(shù)分析方法,選取每種數(shù)據(jù)挖掘方法中最適合的算法。在關(guān)聯(lián)規(guī)則分析方法中,對(duì)其算法Apriori進(jìn)行改進(jìn),引入矩陣的思想,將基于事務(wù)數(shù)據(jù)庫(kù)的字符串運(yùn)算轉(zhuǎn)化為基于矩陣的布爾值運(yùn)算,減少了算法運(yùn)行過(guò)程中對(duì)數(shù)據(jù)庫(kù)的訪問(wèn),釋放了內(nèi)存空間,提高算法運(yùn)行效率。最后以中北大學(xué)圖書(shū)館數(shù)據(jù)庫(kù)中的借閱記錄為基礎(chǔ),利用clementine軟件對(duì)其進(jìn)行數(shù)據(jù)挖掘?yàn)闀?shū)目推薦服務(wù)提供實(shí)例參考。 進(jìn)行數(shù)據(jù)挖掘時(shí),共分?jǐn)?shù)據(jù)預(yù)處理、數(shù)據(jù)挖掘?qū)嵤⑼诰蚪Y(jié)果分析及結(jié)論建議四步進(jìn)行,在四個(gè)步驟中,數(shù)據(jù)挖掘?qū)嵤┦侵攸c(diǎn)階段。本文利用聚類分析、關(guān)聯(lián)規(guī)則分析和決策樹(shù)分析三種方法對(duì)借閱記錄實(shí)施數(shù)據(jù)挖掘,聚類分析和關(guān)聯(lián)規(guī)則分析是從讀者角度對(duì)數(shù)據(jù)進(jìn)行處理,而決策樹(shù)分析是從圖書(shū)種類角度對(duì)數(shù)據(jù)進(jìn)行處理,得到對(duì)該圖書(shū)感興趣的讀者群,然后根據(jù)讀者是否滿足該讀者群的特征,判斷是否應(yīng)該向讀者推薦這種圖書(shū)。其中,引入決策樹(shù)分析方法是圖書(shū)推薦服務(wù)的首次嘗試。
[Abstract]:The rapid development of the Internet has brought a strong impact to people's way of life, and the rich and convenient way of obtaining information has triggered the information revolution in the world. In this context, most commercial websites set up a commodity recommendation system to provide people with more intuitive and effective services, but up to now, the recommended services have not been paid enough attention to in the application of libraries. In order to improve the book recommendation service in the book management, this paper introduces the data mining technology into the library management system. Firstly, the paper compares the research status of the book recommendation system at home and abroad. This paper points out that the library information recommendation service should be divided into several aspects and what technical support is needed, and introduces the commonly used recommended technologies, compares their advantages and disadvantages, and selects the recommended technology suitable for bibliographic recommendation. Then it introduces the data mining methods of bibliographic recommendation in detail: cluster analysis method, association rule analysis method, decision tree analysis method, and select the most suitable algorithm in each data mining method. In the analysis method of association rules, the algorithm Apriori is improved, the idea of matrix is introduced, the string operation based on transaction database is transformed into Boolean operation based on matrix, and the access to database is reduced. The memory space is freed and the efficiency of the algorithm is improved. Finally, based on the borrowing records in the database of the Central North University Library, the author uses the clementine software to mine the data for the bibliographic recommendation service. Data mining is divided into four steps: data preprocessing, data mining implementation, mining result analysis and conclusion and suggestion. Among the four steps, data mining implementation is the key stage. In this paper, we use clustering analysis, association rule analysis and decision tree analysis to implement data mining for loan records. Cluster analysis and association rule analysis deal with data from the perspective of readers. The decision tree analysis is to process the data from the perspective of book type to get the readers who are interested in the book, and then judge whether the reader should recommend the book to the reader according to whether the reader satisfies the characteristics of the reader. Among them, the introduction of decision tree analysis method is the first attempt of book recommendation service.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP311.13

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 錢宏;;數(shù)據(jù)挖掘預(yù)處理技術(shù)的研究[J];電腦知識(shí)與技術(shù);2010年17期

2 耿鑫;劉晉佩;;數(shù)據(jù)挖掘中的推薦算法綜述[J];電腦知識(shí)與技術(shù);2012年19期

3 吳兵;葉春明;;基于效用的個(gè)性化推薦方法[J];計(jì)算機(jī)工程;2012年04期

4 余力,劉魯;電子商務(wù)個(gè)性化推薦研究[J];計(jì)算機(jī)集成制造系統(tǒng);2004年10期

5 常勇生;;國(guó)內(nèi)外數(shù)字圖書(shū)館個(gè)性化信息服務(wù)現(xiàn)狀與建設(shè)趨勢(shì)[J];科技情報(bào)開(kāi)發(fā)與經(jīng)濟(jì);2007年28期

6 陳錦;吳揚(yáng)揚(yáng);;Apriori算法在高校圖書(shū)館圖書(shū)推薦中的應(yīng)用[J];河南科技學(xué)院學(xué)報(bào)(自然科學(xué)版);2012年04期

7 丁雪;;基于數(shù)據(jù)挖掘的圖書(shū)智能推薦系統(tǒng)研究[J];情報(bào)理論與實(shí)踐;2010年05期

8 黃月紅;周秀梅;覃澤;;基于關(guān)聯(lián)規(guī)則的圖書(shū)借閱服務(wù)推薦方法[J];圖書(shū)館界;2010年04期

9 陰江烽;;面向科研學(xué)術(shù)對(duì)象服務(wù)的個(gè)性化圖書(shū)推薦系統(tǒng)研究[J];探求;2012年04期

10 林郎碟;王燦輝;;Apriori算法在圖書(shū)推薦服務(wù)中的應(yīng)用與研究[J];計(jì)算機(jī)技術(shù)與發(fā)展;2011年05期

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