公司要生存在競爭市場中，知道公司的哪些產品可以獲得最大的利潤並推出市場是很重要的。為了達到這個目的，公司不僅需要考慮產品的特性，還需要分析客戶如何做出購買決定。對於大多數客戶來說，產品的價格是最重要的購買因素之一。產品的價格產生變動，客戶的購買決定可能會改變。因此，本論文利用候選產品與市場上已經存在的產品競爭決定候選產品是否具有影響力，也就是能吸引顧客購買的能力。若是候選產品的產品屬性不具影響力，則向下調候選產品的定價，使得候選產品因為低價因素而具有影響力。依循常理來說，產品的影響力應該由市佔率來決定，也就是產品能夠滿足的客戶量來決定，因為客戶是市場供需中的需求方，所以越多客戶喜歡的產品，產品的影響力越高。不過，在競爭市場上，產品有高市佔率不一定就能達到高獲利。因此，如何透過價格的調整找尋一個或一組能獲取最大利潤的產品是研究重點。在本論文中，我們分別研究如何選擇一個與一組的能獲取最高利潤產品。
在考量選擇一個最高利潤產品時，我們利用動態天際線來決定產品是否滿足客戶需求，也就是若產品成為客戶需求的動態天際線則視作產品滿足客戶需求。候選產品需要調整價格時，都要與所有現有產品進行競爭，以確定它是否可以成為某些客戶偏好的動態天際線產品之一。由於計算動態天際線的成本高，我們透過平行計算加速處理。為了平行計算，我們使用基於voronoi-diagram的劃分方法將現有產品集和客戶偏好集分成多個小資料集合。對於小資料集合，可以生成大量的組合。為了進一步提高性能，我們設計了兩種有效的削減策略來避免計算所有的組合，發展出一個可有效率找出最佳獲利產品的方法。
在選擇一組最佳獲利產品時，我們透過客戶需求與產品屬性之間的距離，決定產品是否滿足客戶需求。由於滿足客戶需求的產品可能很多，我們利用距離長短決定客戶最有可能選擇購買的產品。而候選產品需要調整價格時，除了考量所有現有產品進行競爭之外，還要考量候選產品之間的競爭。在此研究中，我們提出了兩種策略。一種是避免處理候選產品的所有可能子集，另一種是避免考慮所有可能的價格設置。實驗結果表明了該策略的有效性。
針對這兩個研究，我們皆設計一系列的實驗，以人造模擬資料及真實資料來驗證我們所提出的演算法的有效性及執行效率。

For a company, knowing which products can obtain the maximum profits is a critical issue. To achieve this goal, companies not only need to consider products’ features but also need to analyze how customers make their purchase decisions. For most customers, the price of the product is one of the most important purchase factors. The price of the product may change, and the customer's purchase decision may change. In this dissertation, we utilize the competition among candidate products and existing products to determine whether a candidate product has influence, that is the ability to attract customers to purchase. If the product attributes of the candidate product are not influential, we can lower the price of the candidate product and make it influential. In general, the influence of a product should be determined by its market share, which is the number of potential customers. However, a high market share of a product may not lead to high profitability. In this dissertation, we concentrate on finding the candidate product(s) with maximum profit through price adjustment. We formulate two problems, one is finding the most profitable candidate product by dynamic skyline, and the other is selecting the most profitable products by distance-based adoption model.
In the first problem, the dynamic skyline of a customer's preference identifies the products that the customer may purchase. Each time the price of a candidate product is adjusted, it needs to compete with all of the existing products to determine whether it can be one of the dynamic skyline products of some customer preferences. To compute in parallel, we use a Voronoi-Diagram-based partitioning method to separate the set of existing products and that of customer preferences into cells. For these cells, a large number of combinations can be generated. For each price under consideration of a candidate product, we process all the combinations in parallel to determine whether this candidate product can be one of the dynamic skyline products of the customer preferences. We then integrate the results to decide the price for each candidate product to achieve the most profit. To further improve the performance, we design two efficient pruning strategies to avoid computing all combinations.
In the second problem, we determine whether a product satisfies a customer based on the distance between the customer's preference and the attributes of the product. Since there may be many products that can satisfy a customer, we use the maximum distance to determine which products a customer is most likely to purchase. When a candidate product is adjusted its price, we not only consider all existing products for competition but also the competition among candidate products. To tackle this problem, we propose two strategies. One is to avoid processing all possible subsets of the candidate products, and the other is to avoid considering all possible price settings.
Furthermore, for both studies, a series of experiments on synthetic datasets and real datasets are performed to show the effectiveness and execution efficiency of our proposed algorithms.

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