本文選題：搜索競價廣告 + 關鍵詞優(yōu)化��； 參考：《山東科技大學》2010年碩士論文

【摘要】：搜索競價廣告是當前互聯(lián)網(wǎng)提供的主要的網(wǎng)絡廣告投放方式和最有效的營銷手段,廣告主通過投放的廣告向用戶展示服務和產(chǎn)品以獲得經(jīng)濟收益,而搜索引擎用戶則通過輸入的查詢關鍵詞與廣告競價關鍵詞的匹配來查詢廣告并查看廣告信息。搜索競價廣告關鍵詞優(yōu)化對于廣告能否準確的被用戶定位并獲得更大的展示機會有著至關重要的作用。目前廣告主的一個普遍需求是自動獲得大量跟廣告相關的且能夠帶來最大收益的竟價關鍵詞以提高廣告的展示機會和轉化幾率。這個需求對應的相關問題即搜索競價廣告關鍵詞優(yōu)化問題。搜索競價廣告關鍵詞優(yōu)化是當今搜索競價廣告領域的研究熱點和難點,它的難點在于如何為廣告生成大量的、相關的并能獲得較高經(jīng)濟效益的競價關鍵詞。 針對目前搜索競價廣告關鍵詞優(yōu)化領域存在的問題,本文提出將廣告關鍵詞優(yōu)化分為三個階段進行處理。第一階段,廣告關鍵詞抽取階段。這一階段的主要目標是根據(jù)搜索競價廣告的特點進行廣告關鍵詞抽取模型的設計并抽取廣告中的關鍵詞作為種子關鍵詞。本文使用基于語言模式挖掘的抽取模型,這種模型能保證種子關鍵詞與廣告具有很高的相關性。第二階段,種子關鍵詞擴展階段。這一階段的主要目標是依據(jù)種子關鍵詞設計廣告關鍵詞擴展模型,以擴展出大量的與種子關鍵詞相關的候選競價關鍵詞集合。本文使用基于概念結構的擴展模型,這種模型能保證生成的關鍵詞數(shù)量眾多并且與種子關鍵詞相關度較高。第三階段,候選競價關鍵詞優(yōu)化選擇階段。這一階段的主要目標是設計優(yōu)化模型對候選競價關鍵詞集合進行優(yōu)化選擇。本文使用基于點擊率預測的優(yōu)化模型,這種模型能保證優(yōu)化結果能夠為廣告主帶來更大的經(jīng)濟收益。 在上述工作的基礎上,本文用實驗驗證了由上述三種模型組成的搜索競價廣告優(yōu)化方法的有效性。首先驗證了基于語言模式挖掘的關鍵詞抽取算法在廣告關鍵詞抽取中優(yōu)于傳統(tǒng)的關鍵詞抽取算法。然后驗證了基于LRM的點擊率優(yōu)化算法也具有較高的準確率。這兩個實驗結果對整個優(yōu)化算法的有效性驗證起到極強的支持作用。最后將搜索競價廣告優(yōu)化方法與主流廣告關鍵詞推薦工具進行了比較實驗,實驗結果顯示,本文的搜索競價廣告優(yōu)化方法生成的競價關鍵詞優(yōu)于主流廣告關鍵詞推薦工具的生成的關鍵詞。
[Abstract]:Search auction advertising is the main online advertising mode and the most effective marketing means provided by the Internet at present. Advertisers display services and products to users through the advertisements they put in order to obtain economic benefits. Search engine users query advertisements and view advertising information by matching the input keywords with the keywords of advertisement bidding. Search auction advertising keyword optimization can be accurately targeted by the user and obtain greater opportunities for display has a vital role. At present, a general demand of advertisers is to automatically obtain a large number of advertising related and can bring the maximum profit of the price keywords to improve the advertising display opportunities and transformation probability. The related problem of this requirement is the optimization problem of search bid advertisement keyword. Search auction advertising keyword optimization is the research hotspot and difficulty in the field of search auction advertising. The difficulty lies in how to generate a large number of relevant and high economic bidding keywords for advertising. Aiming at the problems existing in the field of keyword optimization in search bid advertising, this paper proposes to divide the optimization of advertisement keywords into three stages. The first stage, advertising keyword extraction stage. The main goal of this stage is to design a keyword extraction model according to the characteristics of search advertising and extract keywords as seed keywords. In this paper, the extraction model based on language pattern mining is used, which can ensure the high correlation between seed keywords and advertising. The second stage, seed keyword expansion stage. The main goal of this stage is to design an advertising keyword extension model based on seed keywords to expand a large number of candidate bidding keyword sets related to seed keywords. In this paper, we use an extended conceptual structure model, which can guarantee a large number of generated keywords and high correlation with seed keywords. The third stage, candidate bidding keyword optimization selection stage. The main goal of this stage is to design an optimization model to optimize the selection of candidate bidding keyword sets. In this paper, an optimization model based on the prediction of click rate is used, which can ensure that the optimization results can bring greater economic benefits to advertisers. On the basis of the above work, the effectiveness of the search bidding advertising optimization method composed of the above three models is verified by experiments in this paper. Firstly, it is verified that the keyword extraction algorithm based on language pattern mining is superior to the traditional keyword extraction algorithm in advertising keyword extraction. Then it is verified that the LRM-based click rate optimization algorithm also has a high accuracy. These two experimental results support the validity of the whole optimization algorithm. Finally, the optimization method of search bid advertisement is compared with the mainstream advertising keyword recommendation tool. The experimental results show that, The keywords generated by the search auction advertising optimization method are superior to those generated by the mainstream advertising keyword recommendation tools.
【學位授予單位】：山東科技大學
【學位級別】：碩士
【學位授予年份】：2010
【分類號】：TP391.1

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本文編號：1884909