本文選題：推薦系統(tǒng)　切入點：數(shù)據(jù)稀疏性　出處：《山東大學(xué)》2015年碩士論文

【摘要】：隨著網(wǎng)絡(luò)信息化新格局的出現(xiàn),人們在互聯(lián)網(wǎng)中的角色逐漸發(fā)生了變化。方面,作為信息瀏覽者,可以利用更加豐富的網(wǎng)絡(luò)資源滿足自己的需求。另一方面,作為信息制造者,人們正在習(xí)慣將生活中的點點滴滴上傳到互聯(lián)網(wǎng),同時以史無前例的速度繼續(xù)生產(chǎn)內(nèi)容。這種海量信息的呈現(xiàn)使得用戶無所適從,想要從中挑出真正吻合用戶興趣的內(nèi)容非常困難,這就出現(xiàn)了信息過載現(xiàn)象。所以,當(dāng)下信息過載問題的解決變得日益迫切。推薦系統(tǒng)是解決信息過載問題的關(guān)鍵技術(shù)之一,成為了無數(shù)學(xué)者追逐研究的熱點。推薦系統(tǒng)通過獲取服務(wù)器中用戶的行為日志,得到可以描述用戶興趣的原始數(shù)據(jù),進而構(gòu)建用戶的興趣模型,通過相似度分析計算,為用戶呈現(xiàn)更加個性化的瀏覽頁面,從而提高用戶的瀏覽效率和使用感受。推薦系統(tǒng)不僅僅是一個熱門的理論研究方向,而且作為一種有效的營銷手段已經(jīng)廣泛應(yīng)用于互聯(lián)網(wǎng)。然而,面對越來越復(fù)雜多樣的應(yīng)用場景,推薦系統(tǒng)暴露出了若干問題,如：數(shù)據(jù)稀疏性問題、用戶興趣遷移問題等。本文針對現(xiàn)有技術(shù)存在的問題,研究了電影推薦中的推薦算法,同時研究了基于推薦算法的醫(yī)療冷柜存儲策略,提出有效的解決方案。主要內(nèi)容如下：(1)以電影推薦為應(yīng)用背景,提出了一種基于用戶興趣向量的混合電影推薦算法。眾所周知,基于協(xié)同過濾的推薦算法對于用戶的興趣變化不敏感,同時數(shù)據(jù)稀疏性問題也制約了該算法的發(fā)展。針對這兩個問題,提出了一種新型的基于用戶興趣向量的混合電影推薦算法。①為了解決數(shù)據(jù)稀疏性問題,本文引入了用戶混合興趣向量。從電影特征向量入手,借助用戶的評分矩陣以迭代的方式處理得到用戶的興趣特征向量,根據(jù)得到的用戶興趣向量和用戶的評分信息組成用戶混合興趣向量,進而構(gòu)建用戶相似矩陣,最終根據(jù)傳統(tǒng)的協(xié)同過濾評分方式完成推薦。②針對用戶興趣變化的情況,在構(gòu)建用戶興趣向量過程中融入時間因子,使得越接近當(dāng)前時間的評分行為權(quán)重越大,越能反映出用戶的當(dāng)前興趣。本文在Movielens數(shù)據(jù)集上進行了實驗,并與現(xiàn)有的相關(guān)算法進行了性能比較。實驗結(jié)果表明本文算法在預(yù)測評分準確性和收斂性上都有明顯的提高。(2)以醫(yī)療冷柜為應(yīng)用背景,提出一種基于用戶行為的智能醫(yī)療冷柜系統(tǒng)中樣品的智能存取策略。該策略在智能醫(yī)療冷柜的自動化提取過程中加入樣品推薦模塊,增強用戶與冷柜系統(tǒng)的交互能力,提升用戶的工作效率。具體的,該策略重點解決以下技術(shù)問題：①如何利用豐富的樣品內(nèi)容信息輔助存儲。②如何根據(jù)用戶的存取行為構(gòu)建有效的提取策略。通過實時收集用戶的存儲和提取行為建立用戶的行為數(shù)據(jù),結(jié)合樣品本身特征屬性和用戶行為的數(shù)據(jù)分析,建立樣品之間的關(guān)聯(lián)度矩陣,從而針對待存儲樣品給出合理的存儲位置(問題①)。同時,在用戶提取樣品階段提出相應(yīng)的推薦,從而提升用戶的使用體驗(問題②)。
[Abstract]:With the advent of the new pattern of the information network, on their role in the Internet has gradually changed. As information, visitors can use more cyber source to meet their own needs. On the other hand, as information producers, people are accustomed to the life of the little Didi uploaded to the Internet, at the same time the There was no parallel in history. continues to produce content. This information allows users to pick out the real situation, consistent with the user interested in the content is very difficult, it appeared the phenomenon of information overload. Therefore, when solving the problem of information overload has become increasingly urgent. Recommendation system is one of the key technologies to solve the problem of information overload, has become a focus of countless the scholars chase research. The recommendation system get the server user behavior log, the original data can be obtained to describe user interest, Then construct the user interest model, through the analysis of similarity calculation, the user presents a more personalized browsing page, so as to improve the user's browsing efficiency and experience. The recommendation system is not only a theoretical study on the hot direction, but also as an effective marketing tool has been widely used in the Internet. However, in the face of more and more application scenarios more complex, the recommendation system exposes some problems, such as: data sparsity, user interest migration problems. Aiming at the problems of existing technology, on the recommendation of the movie recommendation algorithm, and studies the medical freezer storage strategy based on recommendation algorithm, propose effective solutions. The main contents are as follows: (1) the movie recommendation for the application background, proposes a hybrid recommendation algorithm based on user interest vector movie. As everyone knows, based on collaborative filtering The filter recommendation algorithm is not sensitive to the user's interest, while the data sparsity problem also restricts the development of the algorithm. To solve these two problems, proposes a new hybrid film recommendation algorithm based on user interest vector. In order to solve the problem of data sparsity, this paper introduces the user interest vector of mixed. From the movie feature vector, get the user interest feature vectors in an iterative manner using score matrix of the user, based on user interest vector mixed user interest vector and users get the score information, then construct the user similarity matrix, according to the final score of traditional collaborative filtering recommendation. For the completion of the change of user interest. Into the time factor in the process of constructing the user interest vector, the score closer to the current time behavior weight is bigger, more can reflect the user's The current interest. We carried out experiments on Movielens data sets, and comparisons with existing algorithms. The experimental results show that this algorithm has obvious improvement in predicting accuracy and convergence. (2) to the medical refrigerator as the application background, the proposed intelligent access strategy of a sample of intelligent medical refrigerator system based on the user behavior in the process of adding samples. The strategy recommendation module in automatic intelligent medical freezer extraction, enhance the interaction ability of user and the refrigerator system, to enhance the user's work efficiency. In particular, the strategy to solve the following technical problems: how to use the sample content information auxiliary storage rich. How to construct extraction effective strategies according to the user's access behavior. The establishment of user behavior data collected by users in real time storage and extraction behavior, combined with the characteristics of the sample itself Data analysis of sex and user behavior, and establishment of correlation matrix between samples, so as to provide reasonable storage location for storage samples (problem 1). At the same time, recommend corresponding recommendation in user sampling stage, so as to enhance user experience.

【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP391.3

【參考文獻】

相關(guān)期刊論文 前1條

1 印桂生;崔曉暉;馬志強;;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;2012年01期

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本文編號：1698735