【摘要】：隨著互聯(lián)網(wǎng)時(shí)代的到來,網(wǎng)絡(luò)上的信息呈現(xiàn)出指數(shù)增長(zhǎng)的趨勢(shì)。醫(yī)療信息資源作為海量網(wǎng)絡(luò)信息中的一部分也呈現(xiàn)出了“爆炸式”的增長(zhǎng)趨勢(shì)。用戶在海量網(wǎng)絡(luò)信息中很難快速地找到自己所需要的有用信息,這就是在所謂的“信息爆炸”和“信息過載”的互聯(lián)網(wǎng)時(shí)代所體現(xiàn)出來的弊端。微軟亞洲研究院負(fù)責(zé)搜索的一名技術(shù)專家說：75%的內(nèi)容通用搜索引擎搜索不出來,僅僅能夠獲取互聯(lián)網(wǎng)中的一小部分信息。與此同時(shí),通用搜索引擎往往返回給用戶幾十頁甚至上百頁的信息,但用戶通常不會(huì)一頁頁的去查看是否是自己所需的信息,于是就導(dǎo)致了用戶真正所需的信息可能出現(xiàn)在幾十頁甚至上百頁之后而并沒有被挖掘推薦出來。這就說明,通用搜索引擎雖然能夠很輕松地幫助我們找到海量的信息,但是我們卻很難從中找到自己真正想得到的信息。為了改善通用搜索引擎的弊端,本文研究并設(shè)計(jì)了一套面向醫(yī)療領(lǐng)域個(gè)性化的醫(yī)療信息推薦系統(tǒng),該系統(tǒng)能夠?qū)⒂脩羲栊畔⒓捌湎嚓P(guān)的信息推薦給用戶,能夠很好的滿足用戶的需求。 本文以數(shù)據(jù)挖掘和信息推薦算法為基礎(chǔ),研究設(shè)計(jì)并實(shí)現(xiàn)了一套專門用于醫(yī)療信息領(lǐng)域的個(gè)性化信息推薦系統(tǒng)。首先,本文詳細(xì)討論了個(gè)性化醫(yī)療信息推薦相關(guān)的關(guān)鍵技術(shù),主要包括用戶興趣模型的構(gòu)建和信息推薦基本算法,重點(diǎn)分析了幾種信息推薦算法的優(yōu)缺點(diǎn),并最終設(shè)計(jì)了一種符合本系統(tǒng)設(shè)計(jì)所需的推薦算法。其次,詳細(xì)闡述了醫(yī)療信息領(lǐng)域中個(gè)性化信息推薦系統(tǒng)的設(shè)計(jì),從系統(tǒng)需求出發(fā),構(gòu)建系統(tǒng)的整體框架,設(shè)計(jì)了用戶興趣模型、中文分詞模塊、信息預(yù)處理模塊、信息推薦模塊以及個(gè)性化頁面定制模塊等。最后,實(shí)現(xiàn)了醫(yī)療領(lǐng)域中的個(gè)性化信息推薦系統(tǒng),并對(duì)實(shí)驗(yàn)結(jié)果進(jìn)行了分析。實(shí)驗(yàn)結(jié)果表明,在實(shí)驗(yàn)室環(huán)境條件下,本系統(tǒng)能夠很好的將用戶所需的醫(yī)療信息推薦出來,并且還能夠推薦給用戶一些相關(guān)的醫(yī)療信息。
[Abstract]:With the advent of the Internet era, the information on the network shows an exponential growth trend. As a part of massive network information, medical information resources also show an explosive growth trend. It is very difficult for users to find the useful information they need quickly in the mass network information, which is the drawback of the so-called "information explosion" and "information overload" in the Internet era. A search technology expert at Microsoft Research Asia said: 75 percent of all content search engines fail to search and can access only a fraction of the information on the Internet. At the same time, generic search engines often return dozens or even hundreds of pages of information to users, but users usually don't page by page to see if it's what they need. As a result, the information the user really needs may appear after dozens or even hundreds of pages without being mined and recommended. This shows that although the general search engine can easily help us find a huge amount of information, it is difficult for us to find the information we really want from it. In order to improve the disadvantages of general search engine, this paper studies and designs a personalized medical information recommendation system for medical field. The system can recommend the information needed by users and related information to users. Be able to meet the needs of users. Based on data mining and information recommendation algorithm, a personalized information recommendation system is designed and implemented in this paper. Firstly, this paper discusses the key technologies related to personalized medical information recommendation in detail, including the construction of user interest model and the basic algorithm of information recommendation, and analyzes the advantages and disadvantages of several information recommendation algorithms. Finally, a recommendation algorithm is designed to meet the need of the system design. Secondly, the design of personalized information recommendation system in the field of medical information is described in detail. Based on the system requirements, the overall framework of the system is constructed, and the user interest model, Chinese word segmentation module, information preprocessing module are designed. Information recommendation module and personalized page customization module. Finally, the personalized information recommendation system in the medical field is implemented, and the experimental results are analyzed. The experimental results show that under the condition of laboratory environment, the system can recommend the medical information needed by the user, and can also recommend some related medical information to the user.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：R319

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本文編號(hào)：2262625