發(fā)布時間：2018-09-07 16:55

【摘要】：21世紀以來,全世界科技水平不斷提高,信息呈現(xiàn)爆炸式增長,人們從尋找信息的模式變成了尋找有用信息的模式。從海量信息中尋找到對自己有用信息的手段有很多,推薦系統(tǒng)是其中最重要也是最廣泛使用的手段之一。各大網(wǎng)商通過使用推薦系統(tǒng)對用戶進行個性化的推薦,都取得了不錯的成果,這也為推薦系統(tǒng)在高等院校圖書推薦領(lǐng)域的應(yīng)用提供了可能性。推薦系統(tǒng)中的算法有很多,其中最經(jīng)典應(yīng)用最廣泛的是協(xié)同過濾算法。本文對基于用戶和基于項目的協(xié)同過濾算法進行了深入研究,針對高校圖書推薦的特殊性,如借閱數(shù)據(jù)而無法直接使用,相似度矩陣太過稀疏而無法產(chǎn)生推薦等問題,改進了這兩種算法。但是這兩個算法在高校圖書推薦領(lǐng)域都有著各自的優(yōu)劣勢,通過對兩者的結(jié)合,提出了混合推薦系統(tǒng)模型。最后通過實驗對比了混合推薦算法與單一推薦算法的各項評價指標,為應(yīng)用于高校圖書推薦提供了理論支撐。本研究的主要工作有以下五個部分:第一部分,深入研究了推薦系統(tǒng)的原理以及一些經(jīng)典的推薦算法,并對推薦算法應(yīng)用在高校圖書推薦領(lǐng)域的可行性進行了分析,然后構(gòu)建了基于讀者(RCF)和基于圖書(BCF)的協(xié)同過濾算法模型。第二部分,由于高校圖書推薦不同于電影推薦或者商品推薦,它不包含用戶對物品的評分,針對這一特點,通過對圖書借閱記錄的處理,提出一種量化評分模型,將讀者對圖書的偏好定量化,在此基礎(chǔ)上,構(gòu)建了讀者-圖書的評分矩陣。第三部分,針對讀者-圖書評分矩陣過于稀疏的特點,將中文圖書分類法與圖書借閱記錄相結(jié)合,構(gòu)建了讀者-圖書類別評分矩陣,然后在此基礎(chǔ)上改進了基于讀者和基于圖書的協(xié)同過濾算法。第四部分,通過對改進后的RCF和BCF結(jié)合,構(gòu)建了混合推薦系統(tǒng)(HCF)模型,然后進行了實驗驗證,并評估了三種算法模型。第五部分,根據(jù)模型的結(jié)果,對高校圖書推薦系統(tǒng)的應(yīng)用提出了自己的建議。
[Abstract]:Since the 21st century, the level of science and technology all over the world has been improved, and the information has been increasing explosively. People have changed from the mode of searching for information to the mode of seeking useful information. There are many ways to find useful information from mass information, and recommendation system is one of the most important and widely used methods. By using the recommendation system, all the network merchants have achieved good results, which provides the possibility for the application of the recommendation system in the field of book recommendation in colleges and universities. There are many algorithms in recommendation system, among which the most classical one is collaborative filtering algorithm. In this paper, the collaborative filtering algorithm based on user and item is studied in depth. Aiming at the particularity of book recommendation in colleges and universities, such as borrowing data and not using it directly, the similarity matrix is too sparse to produce recommendation and so on. These two algorithms are improved. However, these two algorithms have their own advantages and disadvantages in the field of book recommendation in colleges and universities. Through the combination of the two algorithms, a hybrid recommendation system model is proposed. Finally, the evaluation indexes of mixed recommendation algorithm and single recommendation algorithm are compared through experiments, which provides theoretical support for the application of book recommendation in colleges and universities. The main work of this study is as follows: in the first part, the principle of recommendation system and some classical recommendation algorithms are studied in depth, and the feasibility of applying recommendation algorithm in the field of book recommendation in colleges and universities is analyzed. Then a collaborative filtering algorithm model based on reader (RCF) and book (BCF) is constructed. The second part, because the university book recommendation is different from the movie recommendation or the commodity recommendation, it does not contain the user to the article score, in view of this characteristic, through the processing to the book loan record, proposed one kind of quantification score model. On the basis of quantifying readers' preference for books, a reader-book scoring matrix is constructed. In the third part, aiming at the characteristic that the reader-book scoring matrix is too sparse, this paper combines the Chinese book classification method with the book borrowing record, and constructs the reader-book classification scoring matrix. Then the collaborative filtering algorithm based on readers and books is improved. In the fourth part, through the combination of improved RCF and BCF, the (HCF) model of hybrid recommendation system is constructed, and then the experimental verification is carried out, and three kinds of algorithm models are evaluated. In the fifth part, according to the results of the model, the author puts forward some suggestions on the application of the book recommendation system in colleges and universities.
【學(xué)位授予單位】：內(nèi)蒙古大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3

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