對想要拓展客戶群的小型公司而言，經由社群網路，去行銷公司的新產品是方便、
低成本及有效益的。在過去文獻裡，有許多文章探討「影響最大化問題」，此問題
主要是研究挑選一些消費者當作種子去散佈產品的資訊，使得接收此資訊的消費者
數為最多。然而至今，探討「影響最大化問題」的文獻，並未去考量其它消費者的
看法。這些看法會去改變原本消費者的行為意圖。在這篇論文裡，我們考量此因
素，提出了一個新的「影響最大化問題」，名為「有限預算種子挑選問題」。此問
題主要是研究在社群網路中，有限的預算下，挑選一些消費者當作種子去散佈自家
新產品的資訊，期望對此產品其行為意圖高的消費者能夠最多。然而，對「有限預
算種子挑選問題」，我們提出了一個近似演算法。此外，我們延伸此問題，名為
「多個產品及謠言散佈者的有限預算種子挑選問題」，在社群網路中，我們同時考
量多個產品及謠言散佈者。對此「多個產品及謠言散佈者的有限預算種子挑選問
題」，我們也提出了一個近似演算法。另外，我們也去探討「有限預算種子挑選問
題」的變形。最後，對於「有限預算種子挑選問題」及「多個產品及謠言散佈者的
有限預算種子挑選問題」，我們利用真實的紀錄資料去實驗，評估我們所提出的方
法其效能。實驗結果顯示，對於「有限預算種子挑選問題」來說，我們所提出的方
法，除可以得到一個近似最佳解外，其效能也勝過許多貪婪方法。另對於「多個產
品及謠言散佈者的有限預算種子挑選問題」來說，我們所提出的方法，其效能同樣
地也勝過需多貪婪方法。

Marketing through social networks is convenient, low-cost, and beneficial for small companies seeking to expand their customer numbers. In the literature, many studies address the influence maximization problem, which selects initial consumers (seeds) to spread the product information such that the number of consumers receiving the product information (the influenced consumers) is maximized. However, to date, none of these schemes take the beliefs of other persons that could significantly change the consumer's behavioral intention into account. In this thesis, we fill this gap by proposing a new variant of the influence maximization problem, the
Budgeted Seed Selection (BSS) problem, which asks for a set of seeds with the total cost not greater than a given budget in a social network such that the total expected behavioral intentions of the consumers influenced by the selected seeds are maximized. Furthermore, we propose an approximation algorithm for the BSS problem. In addition, we extend the BSS problem, the Budgeted Seed Selection with Multiple Products and Rumors (BSS-MPR) problem, which takes multiple products and rumors into account at the same time in a social network. We also propose an approximation algorithm for the BSS-MPR problem. Moreover, we study the variant of the BSS problem, the BSS problem with regardless of seed and the BSS problem with purchase desire, respectively.Finally, we conduct simulations to evaluate the performance of our algorithm using real traces and synthesis data. Experimental results show that our algorithm evaluates an approximately optimal seed set for the BSS problem and outperforms several greedy algorithms for the BSS problem and the BSS-MPR problem, respectively.

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