【摘要】：互聯(lián)網(wǎng)的迅猛發(fā)展,給人們的生活方式帶來了強大的沖擊,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)卻沒有在圖書館應(yīng)用方面得到足夠的重視。 本文以提高圖書管理中圖書推薦服務(wù)為目的,將數(shù)據(jù)挖掘技術(shù)引入到圖書館管理系統(tǒng)中,文章首先對比了國內(nèi)外圖書推薦系統(tǒng)研究狀況,指出圖書館信息推薦服務(wù)應(yīng)該分為幾個方面,需要哪些技術(shù)支持,并且介紹了常用的推薦技術(shù),對比它們的優(yōu)勢和不足,選擇適合進行書目推薦的推薦技術(shù)。然后詳細(xì)的介紹進行書目推薦的數(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,對其算法Apriori進行改進,引入矩陣的思想,將基于事務(wù)數(shù)據(jù)庫的字符串運算轉(zhuǎn)化為基于矩陣的布爾值運算,減少了算法運行過程中對數(shù)據(jù)庫的訪問,釋放了內(nèi)存空間,提高算法運行效率。最后以中北大學(xué)圖書館數(shù)據(jù)庫中的借閱記錄為基礎(chǔ),利用clementine軟件對其進行數(shù)據(jù)挖掘為書目推薦服務(wù)提供實例參考。 進行數(shù)據(jù)挖掘時,共分?jǐn)?shù)據(jù)預(yù)處理、數(shù)據(jù)挖掘?qū)嵤�、挖掘結(jié)果分析及結(jié)論建議四步進行,在四個步驟中,數(shù)據(jù)挖掘?qū)嵤┦侵攸c階段。本文利用聚類分析、關(guān)聯(lián)規(guī)則分析和決策樹分析三種方法對借閱記錄實施數(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數(shù)據(jù)進行處理,而決策樹分析是從圖書種類角度對數(shù)據(jù)進行處理,得到對該圖書感興趣的讀者群,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。其中,引入決策樹分析方法是圖書推薦服務(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)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP311.13

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