本文選題：個(gè)性化推薦 + 層次分析法　； 參考：《東華大學(xué)》2013年碩士論文

【摘要】：個(gè)性化推薦是一種智能的主動(dòng)的為用戶提供其可能感興趣的產(chǎn)品或者服務(wù)的互聯(lián)網(wǎng)新應(yīng)用,它能夠有效的提高用戶對(duì)產(chǎn)品或者服務(wù)供應(yīng)商的忠誠(chéng)度,提高企業(yè)效益,與此同時(shí),它能減少用戶搜尋其可能感興趣的產(chǎn)品或者服務(wù)所花費(fèi)的時(shí)間和精力,為用戶提供更加智能的服務(wù)。目前個(gè)性化推薦已經(jīng)成功的應(yīng)用于互聯(lián)網(wǎng)服務(wù)領(lǐng)域,如電子商務(wù)、視頻點(diǎn)播服務(wù)、網(wǎng)絡(luò)廣告投放、新聞?dòng)喼频取?上海醫(yī)聯(lián)預(yù)約平臺(tái)是在上海申康醫(yī)院發(fā)展中心開(kāi)展的醫(yī)聯(lián)工程的應(yīng)用基礎(chǔ)上,面向申康所轄市級(jí)醫(yī)院提供包括門(mén)戶技術(shù)平臺(tái)和預(yù)約掛號(hào)服務(wù)的系統(tǒng),它也屬于一種互聯(lián)網(wǎng)服務(wù)領(lǐng)域,但確少有人研究如何把個(gè)性化推薦引入醫(yī)聯(lián)預(yù)約服務(wù)中,并以此來(lái)提高醫(yī)聯(lián)預(yù)約服務(wù)質(zhì)量和用戶體驗(yàn)。 本文首先對(duì)個(gè)性化推薦技術(shù)進(jìn)行了較為全面、深入的分析。分別用戶建模,推薦對(duì)象建模,推薦算法三個(gè)方面介紹了個(gè)性化推薦系統(tǒng),介紹了個(gè)性化推薦系統(tǒng)的三種實(shí)驗(yàn)方法和四個(gè)主要的評(píng)價(jià)指標(biāo),重點(diǎn)闡述了主流推薦算法的實(shí)現(xiàn)方法和優(yōu)缺點(diǎn)。 接著,針對(duì)上海醫(yī)聯(lián)預(yù)約平臺(tái)存在的信息過(guò)載和“冷熱不均”現(xiàn)象,探索了將個(gè)性化推薦引入醫(yī)聯(lián)預(yù)約平臺(tái)的可能性和意義。本文根據(jù)預(yù)約平臺(tái)的數(shù)據(jù)特征和患者的預(yù)約需求,以預(yù)約績(jī)效和出診績(jī)效為指標(biāo),使用層次分析法建立了醫(yī)生績(jī)效模型,然后根據(jù)患者預(yù)約歷史記錄的統(tǒng)計(jì)結(jié)果,結(jié)合協(xié)同過(guò)濾的思想,建立了患者偏好模型。在上述基礎(chǔ)上,本文提出了一種基于組合模型的醫(yī)生推薦算法,并根據(jù)實(shí)驗(yàn)反饋,進(jìn)行了相應(yīng)的改進(jìn)和優(yōu)化。 最后,本文在上海醫(yī)聯(lián)預(yù)約平臺(tái)上,根據(jù)設(shè)計(jì)的醫(yī)生推薦算法設(shè)計(jì)了醫(yī)生推薦系統(tǒng),并詳述了醫(yī)生推薦系統(tǒng)的數(shù)據(jù)庫(kù)設(shè)計(jì)、離線處理算法的實(shí)現(xiàn)、在線推薦算法的實(shí)現(xiàn)和界面設(shè)計(jì)。根據(jù)醫(yī)生推薦系統(tǒng)上線后記錄的患者操作日志對(duì)推薦系統(tǒng)進(jìn)行了評(píng)測(cè)。評(píng)測(cè)結(jié)果表明本文設(shè)計(jì)的醫(yī)生推薦系統(tǒng)的推薦結(jié)果合理有要,且符合患者的預(yù)約需求。
[Abstract]:Personalized recommendation is an intelligent and active new Internet application that provides users with products or services that they may be interested in. It can effectively enhance the loyalty of users to product or service providers and improve the efficiency of enterprises, at the same time, It reduces the amount of time and effort that users spend searching for products or services they may be interested in, and provides more intelligent services for users. At present, personalized recommendation has been successfully applied in the field of Internet services, such as e-commerce, video-on-demand services, online advertising, news customization and so on. On the basis of the application of the Medical Union Project carried out by the Shanghai Shenhang Hospital Development Center, the Shanghai Medical Association booking platform provides a system for the municipal hospitals under the jurisdiction of Shankang, which includes a portal technology platform and an appointment registration service. It is also a kind of Internet service field, but few people study how to introduce personalized recommendation into Medical Union reservation service to improve the service quality and user experience. In this paper, the personalized recommendation technology is analyzed comprehensively and deeply. In this paper, user modeling, recommendation object modeling and recommendation algorithm are introduced respectively, and three experimental methods and four main evaluation indexes of personalized recommendation system are introduced. The implementation method, advantages and disadvantages of the mainstream recommendation algorithm are expounded. Then, in view of the information overload and the phenomenon of "uneven cold and heat", the possibility and significance of introducing personalized recommendation into the platform are explored. According to the data characteristics of the booking platform and the patient's reservation demand, this paper establishes the doctor's performance model by using the AHP, and then according to the statistical results of the patient's appointment history record, the author takes the appointment performance and the visiting performance as the index. Combined with the idea of collaborative filtering, a patient preference model was established. On the basis of the above, this paper proposes a doctor recommendation algorithm based on combinatorial model, and improves and optimizes the algorithm according to the experimental feedback. Finally, this paper designs the doctor recommendation system on the platform of Shanghai Medical Association, and describes the database design of the doctor recommendation system and the realization of off-line processing algorithm. The realization and interface design of online recommendation algorithm. The recommendation system was evaluated according to the patient operation log recorded after the introduction of the doctor recommendation system. The evaluation results show that the recommended results of the doctor recommendation system designed in this paper are reasonable and meet the needs of patients.
【學(xué)位授予單位】：東華大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：TP311.52

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 朱郁筱;呂琳媛;;推薦系統(tǒng)評(píng)價(jià)指標(biāo)綜述[J];電子科技大學(xué)學(xué)報(bào);2012年02期

2 劉建國(guó);周濤;郭強(qiáng);汪秉宏;;個(gè)性化推薦系統(tǒng)評(píng)價(jià)方法綜述[J];復(fù)雜系統(tǒng)與復(fù)雜性科學(xué);2009年03期

3 楊少梅;;層次分析法在員工績(jī)效評(píng)價(jià)中的應(yīng)用[J];華北電力大學(xué)學(xué)報(bào);2006年04期

4 周軍鋒,湯顯,郭景峰;一種優(yōu)化的協(xié)同過(guò)濾推薦算法[J];計(jì)算機(jī)研究與發(fā)展;2004年10期

5 李聰;梁昌勇;馬麗;;基于領(lǐng)域最近鄰的協(xié)同過(guò)濾推薦算法[J];計(jì)算機(jī)研究與發(fā)展;2008年09期

6 王國(guó)霞;劉賀平;;個(gè)性化推薦系統(tǒng)綜述[J];計(jì)算機(jī)工程與應(yīng)用;2012年07期

7 吳顏;沈潔;顧天竺;陳曉紅;李慧;張舒;;協(xié)同過(guò)濾推薦系統(tǒng)中數(shù)據(jù)稀疏問(wèn)題的解決[J];計(jì)算機(jī)應(yīng)用研究;2007年06期

8 劉瑋;;電子商務(wù)系統(tǒng)中的信息推薦方法研究[J];情報(bào)科學(xué);2006年02期

9 鄧愛(ài)林,朱揚(yáng)勇,施伯樂(lè);基于項(xiàng)目評(píng)分預(yù)測(cè)的協(xié)同過(guò)濾推薦算法[J];軟件學(xué)報(bào);2003年09期

10 許海玲;吳瀟;李曉東;閻保平;;互聯(lián)網(wǎng)推薦系統(tǒng)比較研究[J];軟件學(xué)報(bào);2009年02期

相關(guān)博士學(xué)位論文 前2條

1 應(yīng)曉敏;面向Internet個(gè)性化服務(wù)的用戶建模技術(shù)研究[D];中國(guó)人民解放軍國(guó)防科學(xué)技術(shù)大學(xué);2003年

2 孫小華;協(xié)同過(guò)濾系統(tǒng)的稀疏性與冷啟動(dòng)問(wèn)題研究[D];浙江大學(xué);2005年

相關(guān)碩士學(xué)位論文 前1條

1 高建煌;個(gè)性化推薦系統(tǒng)技術(shù)與應(yīng)用[D];中國(guó)科學(xué)技術(shù)大學(xué);2010年

，

本文編號(hào)：1983159