本文關(guān)鍵詞：股票個性化推薦方法研究　出處：《哈爾濱工業(yè)大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 股票推薦方法 個性化 模糊聚類 圖論模型

【摘要】：隨著我國股市規(guī)模的不斷發(fā)展壯大，股市參與者出現(xiàn)了爆炸式增長的勢頭。從我國現(xiàn)階段股民的組成結(jié)構(gòu)來看，大多數(shù)投資者是屬于專業(yè)知識薄弱的中小股民。為了正確引導(dǎo)股市投資者的價值取向，減少盲目投資造成的資源浪費，這就需要為廣大中小股民提供切實可行的投資指導(dǎo)。當(dāng)前的股票推薦方法主要集中在兩類，分別為基于股評的在線股票推薦方法和基于數(shù)理分析的股價預(yù)測模型，它們擁有各自的優(yōu)勢，但缺陷也相當(dāng)明顯，前種方法不能滿足股民個性化股票推薦的需求，后種方法應(yīng)用過程較為復(fù)雜，很難被投資者理解并掌握。因此，股票個性化推薦方法的研究成為當(dāng)下重要的課題。 近年來，電子商務(wù)呈現(xiàn)蓬勃發(fā)展的新局面，，個性化推薦方法被廣泛應(yīng)用于電子商務(wù)領(lǐng)域來針對目標(biāo)用戶進(jìn)行商品推薦。股票可以被看作是一類特殊的商品，基于此，借鑒商品個性化推薦方法的核心思想來構(gòu)建股票個性化推薦模型是一個可行的思路。本文在充分研究國內(nèi)外關(guān)于推薦方法文獻(xiàn)基礎(chǔ)上，提煉出了商品個性化推薦方法的核心思想。通過對原有推薦方法進(jìn)行改良和創(chuàng)新，構(gòu)建了股票個性化推薦模型。建模思路分為兩個步驟：首先，構(gòu)建股民特征指標(biāo)體系并運用模糊聚類方法來進(jìn)行股民群體細(xì)分；其次，運用圖論和信息最大化保留思想來改進(jìn)原有推薦方法，建立了基于模糊聚類的股票個性化推薦方法。完成建模過程后，本文首先運用仿真模擬方法來建立運用于實證過程的股民-股票評分?jǐn)?shù)據(jù)庫，然后，運用編程方法來實現(xiàn)股票個性化推薦流程。最后，將本文推薦方法與基于協(xié)同過濾技術(shù)的推薦方法和隨機(jī)推薦方法進(jìn)行推薦精確度和推薦誤差方面的比較分析，得出本文的股票個性化推薦方法具有較高的推薦精度，是一種高性能的實時在線股票推薦方法。
[Abstract]:With the continuous development of the scale of the stock market in China, the participants in the stock market have explosive growth momentum. Most investors are small and medium-sized investors with weak professional knowledge. In order to guide the value orientation of stock market investors, reduce the waste of resources caused by blind investment. This needs to provide practical investment guidance for the majority of small and medium-sized shareholders. The current stock recommendation methods mainly focus on two types. The online stock recommendation method based on stock review and the stock price prediction model based on mathematical analysis have their own advantages, but the defects are quite obvious. The former method can not meet the needs of shareholders' personalized stock recommendation. The application process of the latter method is more complex and difficult to be understood and mastered by investors. The research on the method of stock individualized recommendation has become an important topic at present. In recent years, e-commerce presents a new situation of vigorous development, personalized recommendation method is widely used in the field of e-commerce to target users to recommend goods. Stocks can be regarded as a special kind of goods. Based on this, it is a feasible way to construct the stock personalized recommendation model based on the core idea of the commodity personalized recommendation method. This paper fully studies the literature about the recommendation method at home and abroad. Through the improvement and innovation of the original recommendation method, the stock personalized recommendation model is constructed. The modeling idea is divided into two steps: first. Construct the index system of shareholders' characteristics and use fuzzy clustering method to subdivide the shareholders' groups; Secondly, using graph theory and information maximization retention to improve the original recommendation method, establish a fuzzy clustering based stock personalized recommendation method. After the completion of the modeling process. This paper uses the simulation method to establish the stockholder-stock rating database used in the empirical process, and then uses the programming method to realize the personalized stock recommendation process. Finally. The accuracy and error of recommendation are compared and analyzed with the recommendation method based on collaborative filtering technology and random recommendation method. It is concluded that the personalized stock recommendation method has high recommendation accuracy and is a high performance online stock recommendation method.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F832.51

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