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

【摘要】：近年來,隨著在線服務技術的逐漸成熟與完善,網(wǎng)絡空間中上的在線服務數(shù)量急劇增多,完成相同或相似功能的在線服務也大量增加。用戶面對服務優(yōu)劣選擇時,用戶不可能將每個在線服務都互相比較后再產(chǎn)生交互;還有某些不法服務提供商提供虛假的服務信息和一些惡意用戶提供了不真實的評價等,導致網(wǎng)絡空間中的在線服務的質量參差不齊,使得用戶難以從大量的服務中方便快捷的選擇優(yōu)質的在線服務。另一方面,用戶在與在線服務產(chǎn)生交互的過程中,由于其消費背景、消費心理、消費愛好等因素的影響,使得用戶主觀偏好不一致,即用戶對服務的評價準則和尺度不一致,甚至可能出現(xiàn)矛盾和沖突。然而,傳統(tǒng)的在線服務評價方法并未考慮用戶評價標準不一致的問題,均是同等地看待所有用戶的評價,而且無法抵制惡意評價的攻擊,因此,常常給用戶帶來誤導性的服務選擇決策。上述問題導致在線服務之間無法比較或者說不具備公平的可比較性,所以迫切需要一種客觀公正的服務優(yōu)劣排序方法可有效的輔助用戶進行在線服務的優(yōu)劣選擇決策。針對用戶評價標準不一致和偏好不一致導致網(wǎng)絡空間中的在線服務之間不具備公正的可比較性,從而用戶難以選擇到滿意的在線服務的問題,論文提出了基于社會選擇理論計算在線服務優(yōu)劣的排序方法。首先,根據(jù)用戶給出的用戶-服務評價矩陣構建群體偏好矩陣;然后,基于群體偏好矩陣和Kemeny社會選擇函數(shù)構建0-1整數(shù)規(guī)劃模型;最后,通過求解該模型可得到服務的最優(yōu)排序結果。該方法聚合個體偏好為群體偏好,決策符合群體大多數(shù)人的偏好且與個體偏好保持最大的一致性。通過理論分析和實驗驗證了該方法的合理性和有效性。實驗表明,該方法能有效地解決在線服務之間的不可比較性問題,實現(xiàn)在線服務的優(yōu)劣排序,并可以有效抵制推薦攻擊,具有較強的抗操縱性。此外,本文還根據(jù)提出的方法和模型,設計并實現(xiàn)了在線服務優(yōu)劣評價系統(tǒng)。
[Abstract]:In recent years, with the maturity and perfection of online service technology, the number of online services in cyberspace has increased dramatically, and the number of online services with the same or similar functions has also increased. When the user is faced with the choice of service advantages and disadvantages, it is impossible for the user to compare each online service with each other and then interact with each other; there are also some illegal service providers that provide false service information and some malicious users provide untrue evaluation, etc. As a result, the quality of online services in cyberspace is uneven, which makes it difficult for users to choose high-quality online services conveniently and quickly from a large number of services. On the other hand, in the process of interaction with online services, the subjective preferences of users are inconsistent due to the influence of their consumption background, consumer psychology, consumer preferences, and so on, that is, the criteria and scales of users' evaluation of services are not consistent. There may even be contradictions and conflicts. However, the traditional online service evaluation methods do not take into account the inconsistency of user evaluation criteria, and treat all users' evaluation equally, and can not resist the attack of malicious evaluation. Often brings to the user the misleading service choice decision. The above problems lead to the lack of comparison or fair comparability among online services, so it is urgent to use an objective and fair ranking method to assist users to make decisions on the advantages and disadvantages of online services. In view of the inconsistency of user evaluation criteria and inconsistency of preferences, there is no fair comparability between online services in cyberspace, which makes it difficult for users to choose satisfactory online services. In this paper, a ranking method is proposed to calculate the advantages and disadvantages of online services based on social selection theory. Firstly, the group preference matrix is constructed according to the user-service evaluation matrix given by the user; then, the 0-1 integer programming model is constructed based on the group preference matrix and Kemeny social selection function. By solving the model, the optimal ranking results of the service can be obtained. This method aggregates individual preferences as group preferences, and makes decisions consistent with the preferences of the majority of individuals and maintaining the greatest consistency with individual preferences. The rationality and validity of the method are verified by theoretical analysis and experiments. Experiments show that this method can effectively solve the problem of incomparability between online services, realize the ranking of the advantages and disadvantages of online services, resist recommendation attacks effectively, and have strong resistance to manipulation. In addition, according to the proposed method and model, the online service evaluation system is designed and implemented.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.52

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