發(fā)布時(shí)間：2018-06-02 06:14

  本文選題：推薦系統(tǒng) + SaaS　； 參考：《西南交通大學(xué)》2015年碩士論文

【摘要】：隨著信息技術(shù)的飛速發(fā)展以及SaaS(軟件即服務(wù),software as service)模式的越發(fā)成熟,越來(lái)越多的中小型企業(yè)加入到信息化的大軍中。汽車(chē)產(chǎn)業(yè)鏈協(xié)同SaaS平臺(tái)為中小型汽車(chē)制造業(yè)提供第三方的信息服務(wù)平臺(tái),提升企業(yè)的信息化程度,提供了信息化的可靠保證。汽車(chē)產(chǎn)業(yè)鏈協(xié)同SaaS平臺(tái)上注冊(cè)企業(yè)超過(guò)了8000家,平臺(tái)運(yùn)營(yíng)商為企業(yè)間的業(yè)務(wù)提供了方便,但平臺(tái)方僅僅為租戶(hù)提供B2B的業(yè)務(wù)信息化是遠(yuǎn)遠(yuǎn)不夠的。針對(duì)于汽車(chē)產(chǎn)業(yè)鏈協(xié)同SaaS平臺(tái)上租戶(hù)日益增長(zhǎng)的業(yè)務(wù)需求以及平臺(tái)自身的發(fā)展,本文在對(duì)原有平臺(tái)功能與數(shù)據(jù)研究的基礎(chǔ)上,提出了建立基于汽車(chē)產(chǎn)業(yè)鏈協(xié)同SaaS平臺(tái)的購(gòu)車(chē)推薦系統(tǒng),為實(shí)現(xiàn)系統(tǒng)的功能,本文主要研究包括了以下幾個(gè)方面。(1)論文先對(duì)汽車(chē)產(chǎn)業(yè)鏈協(xié)同SaaS平臺(tái)由B2B模式向B2C模式發(fā)展的必要性與可行性進(jìn)行分析,提出了建立基于平臺(tái)的購(gòu)車(chē)推薦系統(tǒng)的需求。(2)對(duì)基于汽車(chē)產(chǎn)業(yè)鏈協(xié)同SaaS平臺(tái)的購(gòu)車(chē)推薦系統(tǒng)的參與用戶(hù)所需功能進(jìn)行了分析,針對(duì)每一類(lèi)用戶(hù)提出了系統(tǒng)的功能。對(duì)實(shí)現(xiàn)系統(tǒng)的關(guān)鍵性技術(shù)：個(gè)性化推薦以及系統(tǒng)信息集成技術(shù)進(jìn)行了分析,確定了系統(tǒng)關(guān)鍵技術(shù)的解決方案。(3)為了實(shí)現(xiàn)系統(tǒng)數(shù)據(jù)與平臺(tái)數(shù)據(jù)的集成交互,本文使用了數(shù)據(jù)庫(kù)服務(wù)器的觸發(fā)器與作業(yè)相結(jié)合的策略。該策略同時(shí)針對(duì)了系統(tǒng)向平臺(tái)的數(shù)據(jù)交互以及平臺(tái)向系統(tǒng)進(jìn)行的數(shù)據(jù)集成。(4)為實(shí)現(xiàn)對(duì)消費(fèi)者的個(gè)性化推薦,本文分析了當(dāng)下比較流行了推薦技術(shù),選取了基于聚類(lèi)的協(xié)同過(guò)濾方法,并對(duì)該方法進(jìn)行了實(shí)現(xiàn)。(5)最后,論文通過(guò)采用三層架構(gòu)的B/S模式,實(shí)現(xiàn)了基于汽車(chē)產(chǎn)業(yè)鏈協(xié)同SaaS平臺(tái)的購(gòu)車(chē)推薦系統(tǒng)的主要業(yè)務(wù)功能模塊,并對(duì)上述問(wèn)題的實(shí)現(xiàn)進(jìn)行了詳細(xì)說(shuō)明。在論文最后進(jìn)行了論文工作和后續(xù)的改進(jìn)方向的總結(jié)。
[Abstract]:With the rapid development of information technology and the maturity of SaaS (software as service) model, more and more small and medium-sized enterprises join the army of information technology. The collaborative SaaS platform of automobile industry chain provides the third party information service platform for the small and medium-sized automobile manufacturing industry, promotes the informationization degree of the enterprise, and provides the reliable guarantee of the informationization. There are more than 8000 registered enterprises in the automobile industry chain cooperative SaaS platform. The platform operators provide convenience for the business between enterprises, but it is far from enough for the platform only to provide B2B business informatization for the tenants. In view of the increasing business demand of tenants and the development of the platform itself on the collaborative SaaS platform of automobile industry chain, this paper studies the function and data of the original platform. In order to realize the function of the system, a car purchase recommendation system based on collaborative SaaS platform of automobile industry chain is proposed. This paper mainly includes the following aspects. 1) the necessity and feasibility of the development of collaborative SaaS platform from B2B model to B2C mode are analyzed in this paper. The requirement of establishing a platform-based car purchase recommendation system is put forward. The functions of the participating users of the vehicle purchase recommendation system based on the collaborative SaaS platform of the automobile industry chain are analyzed, and the functions of the system are proposed for each class of users. The key technology to realize the system: personalized recommendation and system information integration technology are analyzed, and the solution of the key technology of the system is determined. In order to realize the integration and interaction between the system data and the platform data, This article uses the database server trigger and the job union strategy. This strategy also aims at the data interaction from the system to the platform and the data integration from the platform to the system. In order to realize the personalized recommendation to consumers, this paper analyzes the popular recommendation technology. This paper selects the collaborative filtering method based on clustering, and implements the method. Finally, the paper realizes the main business function module of the car purchase recommendation system based on the collaborative SaaS platform of automobile industry chain by adopting the three-tier structure of B / S mode. The realization of the above problems is explained in detail. At the end of the paper, the author summarizes the work of the paper and the direction of improvement in the future.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TP311.52;TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 邢春曉;高鳳榮;戰(zhàn)思南;周立柱;;適應(yīng)用戶(hù)興趣變化的協(xié)同過(guò)濾推薦算法[J];計(jì)算機(jī)研究與發(fā)展;2007年02期

2 黃曉濤;數(shù)據(jù)庫(kù)觸發(fā)器實(shí)現(xiàn)數(shù)據(jù)庫(kù)系統(tǒng)的主動(dòng)功能[J];計(jì)算機(jī)應(yīng)用研究;1999年09期

3 賀玲;吳玲達(dá);蔡益朝;;數(shù)據(jù)挖掘中的聚類(lèi)算法綜述[J];計(jì)算機(jī)應(yīng)用研究;2007年01期

4 方東;;SAAS與中國(guó)中小企業(yè)信息化[J];科技信息(科學(xué)教研);2007年14期

5 翟東海;魚(yú)江;高飛;于磊;丁鋒;;最大距離法選取初始簇中心的K-means文本聚類(lèi)算法的研究[J];計(jì)算機(jī)應(yīng)用研究;2014年03期

6 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

7 王宇;王淑營(yíng);;面向汽車(chē)產(chǎn)業(yè)鏈協(xié)同SaaS平臺(tái)的DaaS技術(shù)研究[J];計(jì)算機(jī)工程與設(shè)計(jì);2014年03期

8 白娟;布輝;;基于Web挖掘的智能商務(wù)推薦[J];微計(jì)算機(jī)信息;2010年21期

9 魏育成,王耀南,資剛;MS SQL SERVER數(shù)據(jù)庫(kù)觸發(fā)器技術(shù)的實(shí)際應(yīng)用[J];計(jì)算機(jī)系統(tǒng)應(yīng)用;2000年05期

10 吳春旭;李佳俊;石輝;;一種基于分眾分類(lèi)的協(xié)同過(guò)濾推薦算法[J];計(jì)算機(jī)系統(tǒng)應(yīng)用;2010年05期

相關(guān)博士學(xué)位論文 前1條

1 范黎林;基于產(chǎn)業(yè)鏈協(xié)作平臺(tái)的商務(wù)智能架構(gòu)及數(shù)據(jù)挖掘技術(shù)研究[D];西南交通大學(xué);2009年

，

本文編號(hào)：1967729