【摘要】：現(xiàn)代電子信息技術(shù)的快速發(fā)展使得互聯(lián)網(wǎng)信息呈爆炸方式增長(zhǎng)。然而,信息的急速增長(zhǎng)卻未能給用戶提供方便,海量數(shù)據(jù)給用戶獲取知識(shí)帶來(lái)了極大的障礙。這種障礙使得用戶在信息的海洋中卻無(wú)法找到期望有效知識(shí)。為解決此類問(wèn)題,Web個(gè)性化服務(wù)技術(shù)應(yīng)運(yùn)而生。Web個(gè)性化服務(wù)是將Web技術(shù)與數(shù)據(jù)挖掘技術(shù)相結(jié)合以提高Web站點(diǎn)服務(wù)質(zhì)量為目的的一種服務(wù)模式。個(gè)性化服務(wù)是一種“信息找人”的服務(wù)模式。將Web挖掘技術(shù)應(yīng)用于個(gè)性化服務(wù)系統(tǒng)之中,結(jié)合Web文本挖掘、Web領(lǐng)域本體等技術(shù)進(jìn)一步提高Web站點(diǎn)系統(tǒng)的服務(wù)質(zhì)量。 近年來(lái),在國(guó)內(nèi)外學(xué)者的共同努力之下Web個(gè)性化服務(wù)技術(shù)取得了一系列重大科研成果并形成一套經(jīng)典的個(gè)性化服務(wù)模型。然而,現(xiàn)階段個(gè)性化服務(wù)理論體系仍有許多需要完善的地方,比如系統(tǒng)“冷啟動(dòng)”問(wèn)題、如何高效準(zhǔn)確更新用戶興趣模型的問(wèn)題、個(gè)性化推薦算法研究問(wèn)題等。 為解決以上問(wèn)題,本文在傳統(tǒng)個(gè)性化服務(wù)模型的基礎(chǔ)之上,提出了一種基于效用的Web個(gè)性化服務(wù)模型。本模型引入了效用理論,在其基礎(chǔ)之上提出了一種基于效用的用戶興趣模型更新算法,以探求一種高質(zhì)量的用戶興趣模型根更新算法。對(duì)于傳統(tǒng)個(gè)性化服務(wù)模型系統(tǒng)中普遍存在的“冷啟動(dòng)”問(wèn)題,本文通過(guò)引入三方平臺(tái)登錄模塊,以三方平臺(tái)上豐富的用戶網(wǎng)絡(luò)資源來(lái)構(gòu)建用戶初始興趣模型,使用戶可以快速得到高質(zhì)量個(gè)性化服務(wù)。并且針對(duì)經(jīng)典個(gè)性化推薦算法-K-means算法的不足,本文提出了一種基于協(xié)同聚類的用戶-興趣項(xiàng)雙聚類算法。 通過(guò)模擬實(shí)驗(yàn)表明,本模型能夠提供較高質(zhì)量的個(gè)性化服務(wù),可以滿足Web站點(diǎn)的個(gè)性化服務(wù)建設(shè)需求,具有較高的理論研究?jī)r(jià)值與現(xiàn)實(shí)意義。
[Abstract]:The rapid development of modern electronic information technology makes the Internet information explosive growth. However, the rapid growth of information can not provide convenience to users. This barrier prevents users from finding the desired knowledge in the ocean of information. In order to solve this kind of problem, Web personalization service technology emerges as the times require. Web personalization service is a kind of service mode which combines Web technology and data mining technology to improve the service quality of Web site. Personalized service is a kind of service mode of "information seeking person". The Web mining technology is applied to the personalized service system. Combined with the Web text mining and Web domain ontology technology, the service quality of the Web site system is further improved. In recent years, with the joint efforts of scholars at home and abroad, Web personalized service technology has made a series of important scientific research results and formed a set of classic personalized service model. However, the theoretical system of personalized service still needs to be improved, such as "cold start" problem, how to update user interest model efficiently and accurately, and how to study personalized recommendation algorithm. In order to solve the above problems, this paper proposes a Web personalized service model based on utility based on the traditional personalized service model. The utility theory is introduced in this model, and a Utility based updating algorithm of user interest model is proposed to explore a high quality root updating algorithm of user interest model. For the common "cold start" problem in the traditional personalized service model system, this paper constructs the initial user interest model by introducing the three-party platform login module and using abundant user network resources on the tripartite platform. Enables the user to obtain the high quality personalization service quickly. Aiming at the shortcomings of the classical personalized recommendation algorithm-K-means algorithm, this paper proposes a user-interest double clustering algorithm based on cooperative clustering. The simulation results show that the model can provide high quality personalized service and meet the needs of Web site. It has high theoretical research value and practical significance.
【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】：2014
【分類號(hào)】：TP393.09;TP391.1

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