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| Title: | 利用標籤社會網絡之影響力最大化達到目標式廣告行銷
Influence maximization in labeled social network for target advertising |
| Authors: | 李法賢 |
| Contributors: | 沈錳坤
李法賢 |
| Keywords: | 標籤社會網絡
影響力
社會網絡
極大化
Labeled influence maximization
Influence maximization
Social network |
| Date: | 2010 |
| Issue Date: | 2013-06-27 15:47:07 (UTC+8) |
| Abstract: | 病毒式行銷(viral marketing)透過人際之間的互動，藉由消費者對消費者的推薦，達到廣告效益。而廣告商要如何進行病毒式行銷？廣告商必須在有限資源下從人群中找出具有影響力的人，將產品或是概念推薦給更多的消費者，以說服消費者會採納他們的意見。
利用社會網絡(Social network)，我們可以簡單地將消費者之間的關係用節點跟邊呈現，而Influence Maximization就是在社會網絡上選擇最具有影響力的k個消費者，而這k個消費者能影響最多的消費者。
然而，廣告相當重視目標消費群，廣告目的就是希望能夠影響目標消費群，使目標消費群購買產品。因此，我們針對標籤社會網絡(Labeled social network)提出Labeled influence maximization的問題，不同過往的研究，我們加入了標籤的條件，希望在標籤社會網絡中影響到最多符合標籤條件的節點。
針對標籤社會網絡，我們除了修改四個解決Influence maximization problem的方法，Greedy、NewGreedy、CELFGreedy和DegreeDiscount，以找出影響最多符合類別條件的節點的趨近解。我們也提出了兩個新的方法ProximityDiscount和MaximumCoverage來解決Labeled influence maximization problem。我們在Offline時，先計算節點與節點之間的Proximity，當行銷人員Online擬定行效策略時，系統利用Proximity，Onlin找出趨近解。
實驗所採用的資料是Internet Movie Database的社會網絡。根據實驗結果顯示，在兼顧效率與效果的情況下，適合用ProximityDiscount來解決Labeled influence maximization problem。
Influence maximization problem is to find a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence. But when marketers advertise for some products, they have a set of target audience. However, influence maximization doesn’t take target audience into account.
This thesis addresses a new problem called labeled influence maximization problem, which is to find a subset of nodes in a labeled social network that could influence target audience and maximizes the profit of influence. In labeled social network, every node has a label, and every label has profit which can be set by marketers.
We propose six algorithms to solve labeled influence maximization problem. To accommodate the objective of labeled influence maximization, four algorithms, called LabeledGreedy, LabeledNewGreedy, LabeledCELFGreedy, and LabeledDegreeDiscount, are modified from previous studies on original influence maximization. Moreover, we propose two new algorithms, called ProximityDiscount and MaximumCoverage, which offline compute the proximities of any two nodes in the labeled social network. When marketers make strategies online, the system will return the approximate solution by using proximities.
Experiments were performed on the labeled social network constructed from Internet Movie Database, the result shows that ProximityDiscount may provide good efficiency and effectiveness. |
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| Description: | 碩士
國立政治大學
資訊科學學系
97753017
99 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0097753017 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系] 學位論文
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