本文選題：在線服務(wù) + 社會選擇理論��； 參考：《昆明理工大學(xué)》2017年碩士論文

【摘要】：在線服務(wù)作為一種利用互聯(lián)網(wǎng)技術(shù)向用戶提供線上服務(wù)的方式,已在電子商務(wù)、電子政務(wù)、e-Learning、e-Science等領(lǐng)域迅速普及并得到廣泛應(yīng)用。隨著在線服務(wù)的興起,網(wǎng)絡(luò)上出現(xiàn)了大量具有相似功能的服務(wù)。因此,用戶需要對使用哪些服務(wù)以滿足自身需求進行決策。然而,在互聯(lián)網(wǎng)環(huán)境下,用戶從眾多具有同樣功能的服務(wù)中進行選擇決策存在多方面的困難,使用戶不可避免地面臨不能獲取真正需要的服務(wù)的風(fēng)險:一方面,由于在線服務(wù)數(shù)量龐大,用戶不可能與每一個服務(wù)都有交易經(jīng)驗,需要花費大量時間比較每種服務(wù)的優(yōu)劣。另一方面,由于網(wǎng)絡(luò)的虛擬性使用戶不能對在線服務(wù)有直接的了解和接觸,而商家為了提高自身的信譽,可能向用戶提供不真實的產(chǎn)品信息。因此,用戶需要借助第三方觀點為基礎(chǔ)形成的評價對在線服務(wù)的優(yōu)劣進行判斷就顯得尤為重要。目前在線電子商務(wù)評價方法主要有累加法、平均法、概率法、模糊法、流程法等,但這些方法沒有考慮到用戶評分標(biāo)準(zhǔn)不一致導(dǎo)致不同用戶對同一服務(wù)評分不可比較的問題。為在不同用戶對同一服務(wù)評分不可比較時提高在線服務(wù)評價的客觀性,本文提出了一種基于Schulze社會選擇理論的在線服務(wù)可比較評價方法。首先基于用戶之間的相似性對不完全評分數(shù)據(jù)進行填充;然后根據(jù)填充得到的用戶-服務(wù)評分矩陣計算用戶對在線服務(wù)的偏好關(guān)系;最后利用群決策方法中的Schulze社會選擇函數(shù),通過構(gòu)建加權(quán)有向圖并計算最強路徑得到服務(wù)評價。本文選用Schulze社會選擇函數(shù)進行在線服務(wù)的評價,并對Schulze函數(shù)用于在線服務(wù)評價的適用性和有效性進行了理論分析和實驗驗證。理論分析表明本文提出的方法具備有效的評價方法應(yīng)當(dāng)具有的孔多塞性、單調(diào)性、非獨裁性和操縱復(fù)雜性。實驗結(jié)果也表明Schulze函數(shù)適用于在線服務(wù)的評價。最后,根據(jù)本文提出的方法,設(shè)計實現(xiàn)了在線服務(wù)評價原型系統(tǒng)。
[Abstract]:As a way to provide online services to users using Internet technology, online services have been widely used in the fields of electronic commerce, e-government, e-learning and e-Science. With the rise of online services, a large number of similar services appear on the network. Therefore, users need to make decisions about which services to use to meet their own needs. However, in the Internet environment, there are many difficulties for users to choose and make decisions from a large number of services with the same functions, which make users inevitably face the risk of not obtaining the services they really need: on the one hand, Because of the large number of online services, it is impossible for users to have trading experience with each service, and it takes a lot of time to compare the merits and demerits of each service. On the other hand, because of the virtual nature of the network, users can not directly understand and contact with online services. In order to improve their own credibility, merchants may provide users with untrue product information. Therefore, it is very important for users to judge the merits and demerits of online services by means of the evaluation based on third party views. At present, there are several online e-commerce evaluation methods, such as cumulative method, average method, probability method, fuzzy method, flow method and so on. However, these methods do not take into account the problem that different users can not compare the scores of the same service due to the inconsistency of the user rating criteria. In order to improve the objectivity of online service evaluation when different users' scores on the same service are not comparable, this paper presents an online service comparable evaluation method based on Schulze's social choice theory. Firstly, the incomplete scoring data is filled based on the similarity between users; then the user preference relationship to online services is calculated according to the filled user-service score matrix; finally, the Schulze social selection function is used in group decision making. The service evaluation is obtained by constructing weighted digraph and calculating the strongest path. In this paper, Schulze social selection function is used to evaluate online service, and the applicability and effectiveness of Schulze function in online service evaluation are analyzed theoretically and experimentally. The theoretical analysis shows that the method proposed in this paper has the characteristics of Compressionality, monotonicity, non-autocracy and complexity of manipulation. The experimental results also show that the Schulze function is suitable for online service evaluation. Finally, according to the method proposed in this paper, an online service evaluation prototype system is designed and implemented.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.52

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