本文關(guān)鍵詞： 電子商務(wù)推薦 粗糙集理論 可用性評(píng)估 可用性測(cè)試　出處：《北京郵電大學(xué)》2010年碩士論文　論文類型：學(xué)位論文

【摘要】： 隨著計(jì)算機(jī)的普及和互聯(lián)網(wǎng)的發(fā)展,電子商務(wù)逐漸興起并已經(jīng)在當(dāng)今社會(huì)零售業(yè)中占有較多的比重。圍繞電子商務(wù)進(jìn)行的各種研究方興未艾,推薦系統(tǒng)便是其中一個(gè)重要的研究方向,對(duì)于電子商務(wù)的發(fā)展具有很大的現(xiàn)實(shí)意義。 目前,幾乎所有大型的電子商務(wù)網(wǎng)站,如Amazon,eBay,當(dāng)當(dāng)，，卓越,京東商城等,都不同程度的使用了各種形式的推薦系統(tǒng)。但當(dāng)前對(duì)電子商務(wù)系統(tǒng)的研究主要側(cè)重在各種推薦技術(shù)上面。對(duì)于電子商務(wù)網(wǎng)站推薦的可視化并沒有太多的研究。 本文首先研究了電子商務(wù)網(wǎng)站推薦技術(shù)的現(xiàn)狀。對(duì)當(dāng)前的推薦技術(shù)做了分析比較,同時(shí)提出一種基于粗糙集的用戶購(gòu)買傾向推薦機(jī)制,對(duì)推薦機(jī)制及算法做了研究和設(shè)計(jì)。通過對(duì)用戶瀏覽行為記錄的數(shù)據(jù)經(jīng)過粗糙集理論中的約簡(jiǎn),得出了用戶行為與購(gòu)買傾向之間的聯(lián)系算法。 其次本文通過對(duì)現(xiàn)有研究現(xiàn)狀的分析,對(duì)當(dāng)前主流電子商務(wù)網(wǎng)站做了以可用性測(cè)試技術(shù)為基礎(chǔ)的分析和測(cè)試。得出了當(dāng)前網(wǎng)站推薦可視化系統(tǒng)的不足之處。并設(shè)計(jì)了一個(gè)新的電子商務(wù)網(wǎng)站推薦系統(tǒng)。同時(shí)做了系統(tǒng)的高保真原型并進(jìn)行了可用性評(píng)估測(cè)試,對(duì)新設(shè)計(jì)的推薦系統(tǒng)進(jìn)行了驗(yàn)證。 論文中做了大量的可用性測(cè)試,為系統(tǒng)的設(shè)計(jì)提供了從用戶那里得到的許多建議和方法,最后的驗(yàn)證也說明了新系統(tǒng)的可用性更加出色,但論文中對(duì)于算法的驗(yàn)證沒有進(jìn)行過多的研究,也是后續(xù)需要進(jìn)行改進(jìn)的工作。
[Abstract]:With the popularity of computers and the development of the Internet, electronic commerce is gradually rising and has already occupied a large proportion in the retail trade in today's society. A variety of research on e-commerce is in the ascendant. Recommendation system is one of the important research directions, which has great practical significance for the development of e-commerce. At present, almost all large e-commerce sites, such as Amazon Bay, Dangdang, Excellence, JingDong Mall and so on. At present, the research of E-commerce system is mainly focused on various recommendation technology. There is not much research on the visualization of E-commerce website recommendation. Firstly, this paper studies the status quo of e-commerce website recommendation technology, analyzes and compares the current recommendation technology, and puts forward a recommendation mechanism based on rough set. The recommendation mechanism and algorithm are studied and designed, and the link algorithm between user behavior and purchase intention is obtained by reducing the data of user browsing behavior record through rough set theory. Secondly, this paper analyzes the current research situation. This paper analyzes and tests the current mainstream e-commerce websites based on usability testing technology, and concludes the shortcomings of the current website recommendation visualization system, and designs a new E-commerce website recommendation system. At the same time, the high fidelity prototype of the system is made and the usability evaluation test is carried out. The newly designed recommendation system is verified. In this paper, a large number of usability tests have been done, which provide many suggestions and methods from users for the design of the system. Finally, the verification also shows that the new system has better usability. However, there is not much research on the verification of the algorithm in this paper, and it is also the work that needs to be improved in the future.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2010
【分類號(hào)】：J524

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