本文選題：云服務(wù)　切入點(diǎn)：馬爾科夫　出處：《北京郵電大學(xué)》2015年碩士論文

【摘要】：個(gè)性化推薦在信息系統(tǒng)以及電子商務(wù)領(lǐng)域已經(jīng)是非常成熟的技術(shù)了,而且表現(xiàn)出色。但是,數(shù)據(jù)量的大幅增長(zhǎng),數(shù)據(jù)類(lèi)型的多樣性,不僅給數(shù)據(jù)的存儲(chǔ)和管理帶來(lái)了困難,同樣也成為了個(gè)性化推薦的發(fā)展瓶頸。然而,作為一種新型的計(jì)算模式和服務(wù)提供模式,云計(jì)算為解決海量數(shù)據(jù)的管理,提高推薦速度等提供了一種可行的方法。但是,云計(jì)算的出現(xiàn)則給個(gè)性化推薦帶來(lái)了新的問(wèn)題。 根據(jù)用戶(hù)的個(gè)性化需求,為用戶(hù)推薦滿(mǎn)足其個(gè)性化需求的服務(wù)是個(gè)性化推薦的根本目的。現(xiàn)有的針對(duì)云服務(wù)的個(gè)性化推薦或選擇的方法一定程度上解決了用戶(hù)查找服務(wù)難,選擇合適服務(wù)難的問(wèn)題,但是這些方法依然存在著一定的不足。首先,現(xiàn)存的很多方法,例如基于協(xié)同過(guò)濾的推薦,雖然強(qiáng)調(diào)用戶(hù)的個(gè)性化需求,但是沒(méi)有明確的指明用戶(hù)的個(gè)性化到底是什么；其次,用戶(hù)的需求并非一成不變,而是動(dòng)態(tài)變化的,但是大多方法并沒(méi)有明確清楚的區(qū)分用戶(hù)的當(dāng)前需求和非當(dāng)前需求的不同；第三,隨著云計(jì)算影響的深入,用戶(hù)的需求不單純的集中在功能的方面,與傳統(tǒng)PC應(yīng)用相比,云平臺(tái)上的應(yīng)用的實(shí)時(shí)性能將會(huì)成為用戶(hù)關(guān)注的重點(diǎn),所以用戶(hù)的需求包括功能需求和性能需求兩個(gè)方面。第四,用戶(hù)同時(shí)有的需求不止一個(gè),但是現(xiàn)有的很多方法都沒(méi)有區(qū)分對(duì)待這些需求。而現(xiàn)有的方法中能夠綜合考慮上述四個(gè)問(wèn)題的更少,因此,本文提出了一種基于馬爾可夫模型和多屬性決策的云服務(wù)個(gè)性化推薦模型,希望能夠在一定程度上解決所提出的問(wèn)題。通過(guò)對(duì)用戶(hù)使用服務(wù)習(xí)慣的分析,將用戶(hù)使用服務(wù)的過(guò)程抽象為馬爾科夫模型。此外,針對(duì)服務(wù)存在的多個(gè)屬性,提出了一種多效用合并的方法。通過(guò)實(shí)驗(yàn),指標(biāo)所顯示的結(jié)果表明本文所提出的方法達(dá)到了在一定程度上解決所提出問(wèn)題的目的,并且在一定程度提高了推薦性能。
[Abstract]:Personalized recommendation is a very mature technology in the field of information system and electronic commerce, and it has done well. However, the huge increase of data volume and the diversity of data types not only bring difficulties to the storage and management of data. However, as a new model of computing and service provision, cloud computing provides a feasible way to solve the management of massive data and improve the speed of recommendation. However, as a new model of computing and service delivery, cloud computing provides a feasible way to improve the speed of recommendation. The emergence of cloud computing brings new problems to personalized recommendation. According to the individual demand of users, the basic purpose of personalized recommendation is to recommend the services to meet their personalized needs. The existing methods of personalized recommendation or selection for cloud services to some extent solve the difficulty of finding services for users. It is difficult to choose the right service, but these methods still have some shortcomings. First, many existing methods, such as collaborative filtering of recommendations, although emphasizing the personalized needs of users, Second, the user's needs are not fixed, but dynamic, but most of the methods do not clearly distinguish the user's current needs and non-current needs. Third, with the impact of cloud computing, the needs of users are not simply focused on the functional aspects, compared with traditional PC applications, the real-time performance of applications on the cloud platform will become the focus of attention. So the requirements of users include two aspects: functional requirements and performance requirements. Fourth, users have more than one requirement at the same time. But many of the existing methods do not distinguish between these requirements, and fewer of the existing methods are able to take these four issues into account, so, This paper presents a personalized recommendation model for cloud services based on Markov model and multi-attribute decision making, which hopes to solve the problems to some extent. The process of users using services is abstracted as Markov model. In addition, a multi-utility merging method is proposed for the existence of multiple attributes of services. The results show that the proposed method achieves the purpose of solving the problem to a certain extent and improves the performance of recommendation to a certain extent.
【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】：2015
【分類(lèi)號(hào)】：TP391.3;TP393.09

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