過社群媒體進行行銷是目前的趨勢，不但方便而且兼具低成本的優點，對於急於擴大市場的公司是非常有吸引力的。在文獻中，許多研究解決了一種或多種產品的影響最大化問題，其多半選擇一批初始消費者來傳播產品信息，去增加接收到信息的消費者（受影響的消費者）數量。但鮮少人同時考慮到其他人的惡意謠言和行為意圖(Behavior Intention)。
在本文中，作者通過提出多種產品的影響最大化問題的新變體，即限定預算行為意圖最大化問題(Budgeted Behavioral Intentions Maximization Problem)填補了這一空白，該問題要求在不超過給定預算的情況下選擇一組種子(初始消費者)，使得受所選種子影響的消費者的總體預期行為意圖最大化。
此外，作者提出了一種貪婪算法的變體，用來處理這個問題，並以真實社群資料和模擬數據，進行模擬評估演算法的性能。實驗結果表明，該演算法優於多種貪心算法。

Marketing through online social networks is convenient, low-cost, and bene-?cial for companies seeking to expand their customer numbers. In the literature, many studies address the in?uence maximization problem with one or multiple products, which selects initial consumers (seeds) to spread one or multiple product information such that the number of consumers receiving this product information (the in?uenced consumers) is maximized. However, to date, none of these schemes take the rumors and the beliefs of other persons that could signi?cantly change the consumer’s behavioral intention into account at once.
In this paper, the author ?lls this gap by proposing a new variant of the in?uence maximization problem with multiple products, the Budgeted Behavioral Intentions Maximization problem, which asks for a set of seeds with the total cost not greater than a given budget in online social networks such that the total expected behavioral intentions of the consumers in?uenced by the selected seeds and the rumors are maximized. In addition, the author proposes a variant of greedy algorithm for the Budgeted Behavioral Intentions Maximization problem. the also conduct simulations to evaluate the performance of the algorithm using real traces and synthesis data. Experimental results show that the algorithm outperforms several greedy algorithms.

中文摘要 I
Abstract II
Content III
1 Introduction 1
2 Related Works 4
3 Budgeted Behavioral Intentions Maximization Problem 7
3.1 Scenario 7
3.2 Fishbein Model 8
3.3 Propagation 10
3.3.1 Seed Propagation 10
3.3.2 Rumor Propagation 12
3.4 The Problem Definition 13
4 Approximation Algorithm 15
4.1 Enumeration Algorithm 15
4.2 GuessGreedy Algorithm 18
5 Experimental Result 20
5.1 Simulation Settings 20
5.2 Simulation Results 26
6 Conclusion 32

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