社會意見在塑造我們購買決策和購買經歷發揮了至關重要的作用。除了正面（或負面）的意見會鼓勵（或打消）我們購買某個產品，我們的意見更傾向於遵循我們的社交圈。社會意見的這些方面對於做出精確產品推薦、準確預測信息流向、及有效營銷活動發布極為重要。 / 在這篇論文中，我們首先研究極性意見對我們的購買決策的影響。同時，我們分析了兩個現實世界中的社會網絡，Flixster 和Epinions 中的消極和積極的意見的信息傳播模式。我們觀察到，否定意見的存在大大降低了表達意見的數量。考慮到這兩種意見的不對稱性，我們提出並擴展了目前最流行的兩個信息傳播模式，獨立分級和線性閾值模型。我們提出的拓展模型提供了一個可處理的影響問題和並能夠提高將來意見的預測精度，超過3%。 / 更進一步，我們研究了社會意見對我們表達產品意見的影響。該問題的假設是多次顯示我們表達的意見並不完全獨立於我們的社交圈，而是通過校準，使之跟社會意見相似。為了理解這一現象，我們為用戶的評分提出了一個新型的模型。該模型中，用戶對項目的評分是由社會輿論、用戶的偏好和項目特點的一個函數。該模型可以提高用戶評分的預測準確率達2%。此外，模型中學習到的參數可展示用戶對社會意見的遵循程度。用戶對社會意見的遵循分析表明，超過76%的用戶傾向於在一定程度上遵循他們好友的意見。平均而言，當社會影響存在的時候，用戶評分更趨於正面。我們還發現，社會的遵循者通常不是信息傳播的第一次參與者。 / Social opinions play a crucial role in shaping both our purchase decisions and our experience. While on one hand, we are encouraged (discouraged) to adopt a product upon hearing the positive (negative) opinions; on the other hand, our opinions tend to conform to our social circle. Both of these aspects of social opinions are important in order to make precise product recommendations, to accurately predict the information flow pathways and to launch efficient viral marketing campaigns. / In this thesis, we first study the impact of polarity of opinions on our purchase decisions. For the same, we analyze the information propagation patterns of the negative and positive opinions on two real world social networks, Flixster and Epinions, and observe that the presence of negative opinions significantly reduces the number of expressed opinions. To account for the asymmetry between the two kinds of opinions, we propose extensions of the two most popular information propagation models, Independent Cascade and Linear Threshold models. The proposed extensions give a tractable influence problem and improve the prediction accuracy of future opinions, by more than 3%. / Next, we study the impact of social opinions on our expressed opinions about the products. The hypothesis is that many times our expressed opinions are not completely independent of our social circle and gets calibrated such that they are similar to the social opinions. In order to understand this phenomenon, we propose a novel formulation for the users ratings where every expressed rating is considered as a function of the social opinion along with the user preference and item characteristics. The proposed method helps in improving the prediction accuracy of users’ rating by more than 2% in presence of social influence. Additionally, the learned model parameters reveal the degree of conformity of users. Detailed analysis of user social conformity show that more than 76% of users tend to conform to their friends to some extent. On an average, user ratings become more positive in presence of the social influence. We also nd that the social conformers are usually not the rst one to participate in an information cascade. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Garg, Priyanka. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 100-110). / Abstracts also in Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Contributions --- p.3 / Chapter 1.2 --- Organization --- p.4 / Chapter 2 --- Background & Survey --- p.6 / Chapter 2.1 --- Network Structure --- p.6 / Chapter 2.1.1 --- Basic Definitions --- p.6 / Chapter 2.1.2 --- Structural Properties of Social Networks --- p.8 / Chapter 2.1.3 --- Network Generators --- p.12 / Chapter 2.2 --- Information Diffusion in Social Networks --- p.16 / Chapter 2.2.1 --- Basic Terminologies --- p.19 / Chapter 2.2.2 --- Principles governing the Decision-Making process --- p.19 / Chapter 2.2.3 --- Information Cascade Models --- p.21 / Chapter 2.2.4 --- Influence Estimation --- p.34 / Chapter 2.2.5 --- Viral Marketing --- p.39 / Chapter 2.2.6 --- Influence vs. Homophily --- p.44 / Chapter 2.2.7 --- Results from Large Scale Empirical Studies --- p.45 / Chapter 3 --- Impact on Product Purchase Decision --- p.47 / Chapter 3.1 --- Introduction --- p.47 / Chapter 3.2 --- Related Work --- p.49 / Chapter 3.3 --- Problem Definition --- p.50 / Chapter 3.4 --- Data and Observations --- p.51 / Chapter 3.4.1 --- Data Collection --- p.51 / Chapter 3.4.2 --- Observations --- p.52 / Chapter 3.5 --- Polarity-Sensitive Information Flow Model --- p.54 / Chapter 3.5.1 --- Social Influence Function --- p.55 / Chapter 3.5.1.1 --- Polarity-Sensitive IC Model --- p.55 / Chapter 3.5.1.2 --- Polarity-Sensitive LT Model (LTPS) --- p.58 / Chapter 3.5.2 --- Activation State of Influenced Node --- p.59 / Chapter 3.6 --- Influence Estimation --- p.61 / Chapter 3.7 --- Experiments on Synthetic Data --- p.63 / Chapter 3.7.1 --- IA and WP as Approximation of IC-N --- p.64 / Chapter 3.7.2 --- Quality of the Estimated Parameters --- p.65 / Chapter 3.7.3 --- Prediction Accuracy --- p.66 / Chapter 3.8 --- Experiments on Real Data --- p.69 / Chapter 3.8.1 --- Experimental Setup --- p.69 / Chapter 3.8.2 --- Observations --- p.70 / Chapter 3.9 --- Summary --- p.72 / Chapter 4 --- Impact on Posterior Evaluation --- p.73 / Chapter 4.1 --- Introduction --- p.73 / Chapter 4.2 --- Related Work --- p.75 / Chapter 4.3 --- Ratings under social conformity --- p.77 / Chapter 4.3.1 --- Problem Definition & Notations --- p.79 / Chapter 4.3.2 --- Conformer's Ratings --- p.80 / Chapter 4.3.3 --- Parameter Estimation --- p.82 / Chapter 4.4 --- Evaluation --- p.84 / Chapter 4.4.1 --- Goodreads Dataset --- p.85 / Chapter 4.4.2 --- Prediction Accuracy --- p.87 / Chapter 4.4.3 --- Influencers Quality --- p.89 / Chapter 4.5 --- Social Conformity Analysis --- p.91 / Chapter 4.6 --- Summary --- p.96 / Chapter 5 --- Summary & Future Work --- p.97 / Chapter 5.1 --- Summary --- p.97 / Chapter 5.2 --- Future Work --- p.98 / Bibliography --- p.100
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_328661 |
| Date | January 2012 |
| Contributors | Garg, Priyanka., Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | electronic resource, electronic resource, remote, 1 online resource (xii, 110 leaves) : ill. |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
Page generated in 0.0041 seconds