本文選題：用戶體驗質量 + 用戶觀看習慣��； 參考：《南京郵電大學》2017年碩士論文

【摘要】：計算機網絡快速發(fā)展的當下,多樣性的視頻服務源源不斷的涌現,其中IPTV作為一個廣受關注的業(yè)務,被推向了這個技術時代的尖端。在IPTV業(yè)務快速發(fā)展的同時,隨之而來的是用戶對于服務質量的要求。目前的發(fā)展態(tài)勢要求服務提供商們能夠主動評價并預測用戶對IPTV服務的滿意度,從而能夠及時改善不良的用戶體驗。然而傳統的服務質量(QoS)監(jiān)測體系無法滿足要求,需要引入體驗質量(QoE)來描述用戶感知狀況。因此,QoE已經成為當下的研究熱點,尋找提升QoE的解決方案和建立QoE評估模型的需求也越來越迫切�；诖�,本論文主要在用戶主觀指標及主觀評價方法改進、算法選擇與改進、用戶QoE評測系統的設計與實現三方面開展研究工作,具體內容如下:首先,基于機頂盒采集到的用戶觀看記錄數據集,從用戶主觀角度出發(fā),提出用戶觀看習慣指標,此指標反映用戶在觀看節(jié)目時的興趣或愛好。針對傳統主觀評價方法復雜,成本高,非實時的缺點,根據用戶行為指標來改進主觀評價方法,將用戶觀看時間比指標映射為MOS值,實現了QoE的量化。其次,針對所分析的數據特點和實際場景任務的需求,首先比較多種經典算法,包括回歸、k近鄰(kNN)和分類與回歸樹(CART),結果表明CART算法的預測準確度更高。基于此,為了進一步提高模型的預測準確度,對CART算法進行了改進。即,采用核函數的方法改進CART算法的輸出。結果表明,本論文所提出的CART改進算法可以有效地提高QoE模型的預測準確度。最后,基于大數據平臺,針對機頂盒數據集,進行分布式數據存儲、處理和分析,并綜合本論文的評價模型,設計和實現可視化的用戶QoE評測系統。此系統有利于服務提供商及時監(jiān)測服務質量,優(yōu)化網絡環(huán)境,提升用戶體驗,推廣品牌價值,也有利于用戶及時了解服務以及自己對服務的滿意程度和觀看興趣,并可針對服務存在的不足提出自己的建議。
[Abstract]:At the moment of the rapid development of the computer network, the diversity of video service has come to a steady stream. As a widely concerned business, IPTV has been pushed to the tip of the technical age. At the same time, the rapid development of IPTV service is the requirement of the service quality of the users. The current development trend requires service providers It is able to actively evaluate and predict users' satisfaction with IPTV services so that the bad user experience can be improved in time. However, the traditional quality of service (QoS) monitoring system can not meet the requirements and needs to introduce the experience quality (QoE) to describe the user perception. Therefore, QoE has become the focus of the present research, looking for solutions to improve the QoE. And the requirement of establishing QoE evaluation model is becoming more and more urgent. Based on this, this paper mainly focuses on the improvement of user subjective index and subjective evaluation method, algorithm selection and improvement, the design and implementation of user QoE evaluation system in three aspects. On the subjective angle of the user, the user's viewing habits are put forward. This index reflects the interest or hobby of the user when watching the program. The subjective evaluation method is complex, high cost and non real time. According to the user behavior index, the subjective evaluation method is improved, and the user viewing time is mapped to the MOS value, and the quantization of QoE is realized. Secondly, in view of the analysis of the characteristics of the data and the needs of the actual scene task, first compare a variety of classical algorithms, including regression, k nearest neighbor (kNN) and classification and regression tree (CART), the results show that the prediction accuracy of the CART algorithm is higher. Based on this, in order to further improve the prediction accuracy of the model, the CART algorithm is improved. That is, the kernel is adopted. The method of function improves the output of the CART algorithm. The results show that the CART improved algorithm proposed in this paper can effectively improve the prediction accuracy of the QoE model. Finally, based on the large data platform, the distributed data storage, processing and analysis are carried out for the set of set-top box data sets, and the evaluation model of this paper is integrated, and the visualization is designed and implemented. User QoE evaluation system. This system helps service providers to monitor service quality in time, optimize network environment, improve user experience, promote brand value, also help users to understand service as well as their satisfaction and interest in service, and put forward their own suggestions on the shortage of service.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN949.292

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本文編號：2020887