本文關(guān)鍵詞：基于證券理財(cái)產(chǎn)品用戶行為分析的個(gè)性化推薦研究　出處：《電子科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 個(gè)性化推薦系統(tǒng) 證券理財(cái)產(chǎn)品 推薦算法 人類動(dòng)力學(xué)

【摘要】：在大數(shù)據(jù)的時(shí)代背景下,零售、金融和醫(yī)療等傳統(tǒng)行業(yè)開始向“數(shù)據(jù)驅(qū)動(dòng)型企業(yè)”轉(zhuǎn)型,他們意識(shí)到,對(duì)用戶在線行為的記錄以及對(duì)其意圖和偏好的挖掘可以為企業(yè)的運(yùn)營和營銷提供強(qiáng)有力的支持。證券行業(yè)自身的業(yè)務(wù)形態(tài)產(chǎn)生了大量質(zhì)量高、價(jià)值大的數(shù)據(jù),具有極大的挖掘價(jià)值。隨著人們理財(cái)意識(shí)的加強(qiáng),對(duì)于用戶理財(cái)行為特征的研究也逐漸受到更多關(guān)注。隨著科技的迅猛發(fā)展,證券用戶可以獲取的信息量爆炸性的增長,但我們選擇信息的能力有限,如何通過個(gè)性化推薦技術(shù)解決證券用戶遭遇的信息過載現(xiàn)象,成為了證券公司和理財(cái)用戶急需解決的問題。本文既是在此背景下,通過分析證券理財(cái)產(chǎn)品用戶的行為模式,研究適用于理財(cái)產(chǎn)品的個(gè)性化推薦方式,進(jìn)而形成一個(gè)完整的證券理財(cái)個(gè)性化推薦系統(tǒng)。本文的主要工作可以概括為以下三點(diǎn):(1)分析了用戶購買理財(cái)產(chǎn)品的數(shù)據(jù),統(tǒng)計(jì)了用戶購買次數(shù)、理財(cái)產(chǎn)品銷售情況和用戶的活躍性,并從人類動(dòng)力學(xué)的角度挖掘了用戶購買理財(cái)產(chǎn)品的行為特征,同其他人類行為一樣,主要表現(xiàn)為“強(qiáng)陣發(fā)弱記憶”的特性。(2)研究了常用的協(xié)同過濾和混合擴(kuò)散算法,并將這些算法應(yīng)用于證券理財(cái)產(chǎn)品的個(gè)性化推薦中,同時(shí)根據(jù)證券理財(cái)用戶的使用場(chǎng)景,提出了基于用戶聚類的熱門推薦和基于用戶實(shí)時(shí)行為的個(gè)性化推薦兩種擴(kuò)展的推薦策略。(3)參與設(shè)計(jì)并實(shí)現(xiàn)了基于證券理財(cái)產(chǎn)品的個(gè)性化推薦系統(tǒng),詳細(xì)說明了個(gè)性化理財(cái)產(chǎn)品營銷子模塊的框架設(shè)計(jì),以及離線分析和在線推薦的處理流程,通過增量推薦的方式達(dá)到秒級(jí)內(nèi)的響應(yīng)時(shí)間,并從算法和營銷兩個(gè)角度,實(shí)現(xiàn)了個(gè)性化推薦算法在推薦效果上的提升。
[Abstract]:In the context of the era of big data, traditional industries such as retail, finance and healthcare began to shift to "data-driven enterprises," and they realized that. The recording of users' online behavior and the mining of their intentions and preferences can provide strong support for the operation and marketing of enterprises. The business form of the securities industry produces a large amount of high-quality and valuable data. With the strengthening of people's awareness of financial management, the research on the characteristics of user's financial management behavior has gradually received more attention. With the rapid development of science and technology. The amount of information that securities users can obtain increases explosively, but our ability to choose information is limited, how to solve the information overload phenomenon encountered by securities users through personalized recommendation technology. This paper is based on the analysis of the behavior patterns of the users of securities financial products, and studies the individualized recommendation methods suitable for the financial products. The main work of this paper can be summarized as the following three points: 1) analyze the data of the purchase of financial products by users, and count the number of purchases by users. The sales of financial products and the activity of users, and from the perspective of human dynamics to explore the behavior of users to buy financial products, just like other human behavior. The characteristics of "strong burst weak memory". (2) the common collaborative filtering and mixed diffusion algorithms are studied, and these algorithms are applied to the personalized recommendation of securities financing products. At the same time, according to the use of securities financial users. In this paper, two extended recommendation strategies based on user clustering and personalized recommendation based on real-time behavior are proposed to design and implement the personalized recommendation system based on securities financial products. The frame design of marketing sub-module of personalized financial management product and the processing flow of offline analysis and online recommendation are described in detail. The response time within seconds is achieved by incremental recommendation. And from the two angles of algorithm and marketing, the personalized recommendation algorithm in the promotion of recommendation effect.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP391.3;F830.9

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本文編號(hào)：1414111