在线关键字拍卖Agent竞价策略研究
发布时间：2018-09-04 09:20
【摘要】：随着互联网的发展，，搜索引擎已经成为人们快速搜索信息的重要工具，而关键字广告作为搜索引擎的重要经济基础之一，有效地满足了广告主的营销需要，同时也给搜索引擎提供商带来巨大的利润。关键字广告拍卖不仅成为微观经济理论在互联网上最成功的应用之一，而且推动了科学领域的众多学者对其背后机制的研究，成为电子商务领域的研究热点。交易智能体竞赛，简称TAC，是由卡耐基梅隆大学等学校联合主办，旨在模拟真实的市场行为，将目前人工智能的理论研究成果应用到现实的交易过程中。TAC/AA是虚拟关键字广告拍卖平台，研究者可以将研究成果应用到该平台上，验证算法的有效性。本文围绕TAC/AA平台设计了一种能够制定连续最优竞价策略的Agent模型，并提出了动态多样精英PSO算法，有效解决了Agent模型中优化问题的局部收敛问题。 首先本文分析了国内外关键字拍卖的研究现状，介绍了TAC平台的设计思想和比赛的规则，然后研究了关键字拍卖理论、Agent理论和进化算法的相关理论。针对TAC平台的关键字拍卖竞赛，设计了TAC-HEU-AA(THA)Agent模型，分析了预测器的算法的效率，通过实验讨论了各个模块的必要性，并将THA模型与参与TAC/AA决赛的Agent进行比较试验，验证了模型的有效性，分析了模型的优缺点。针对THA模型中的优化器的多选择背包问题(MCKP，Multi-Choice Knapsack Problem)，提出了一种新的基于多样精英选择策略的粒子群算法(DME-PSO)。定义了3种粒子的运动趋势和4种粒子间的运动行为。根据粒子的运动行为选择粒子以保持种群的多样性。在变异过程中，描述了种群的精英饱和现象，在种群精英饱和状态时对种群加入扰动。在实验中，将DME-PSO算法与贪婪算法，多种群遗传算法和加入高斯扰动的粒子群算法进行比较。实验表明DME-PSO算法在物品数量增加时表现出较好的优化效果，从而更好的解决组合优化的局部收敛问题。
[Abstract]:With the development of Internet, search engine has become an important tool for people to search for information quickly. As one of the important economic foundations of search engine, keyword advertisement meets the marketing needs of advertisers effectively. At the same time, it also brings huge profits to search engine providers. Keyword advertising auction has not only become one of the most successful applications of microeconomic theory on the Internet, but also promoted the research of the mechanism behind it by many scholars in the field of science, and has become a research hotspot in the field of electronic commerce. The transaction Agent Competition, or TAC, is sponsored by schools such as Carnegie Mellon University to simulate real market behavior. Applying the current theoretical research results of artificial intelligence to real transactions. TAC / AA is a virtual keyword advertising auction platform. Researchers can apply the research results to this platform to verify the effectiveness of the algorithm. This paper designs a Agent model based on TAC/AA platform, which can formulate continuous optimal bidding strategy, and proposes dynamic and diverse elite PSO algorithm, which effectively solves the problem of local convergence of optimization problem in Agent model. Firstly, this paper analyzes the research status of keyword auction at home and abroad, introduces the design idea of TAC platform and the rules of competition, and then studies the theory of keyword auction and the related theories of evolutionary algorithm. Aiming at the keyword auction competition of TAC platform, this paper designs the TAC-HEU-AA (THA) Agent model, analyzes the efficiency of the algorithm of the predictor, discusses the necessity of each module through experiments, and compares the THA model with the Agent participating in the TAC/AA finals. The validity of the model is verified and the advantages and disadvantages of the model are analyzed. Aiming at the multi-selection knapsack problem of optimizer in THA model (MCKP,Multi-Choice Knapsack Problem), a new particle swarm optimization algorithm (DME-PSO) based on multiple elitist selection strategies is proposed. The movement trends of three kinds of particles and the motion behavior of four kinds of particles are defined. The particles are selected according to their motion behavior to maintain the diversity of the population. In the process of variation, the phenomenon of elite saturation of population is described, and the disturbance is added to the population in the state of elite saturation. In the experiment, DME-PSO algorithm is compared with greedy algorithm, multi-population genetic algorithm and particle swarm optimization algorithm with Gao Si disturbance. The experimental results show that the DME-PSO algorithm performs well when the number of items increases, thus solving the problem of local convergence of combinatorial optimization better.
【学位授予单位】：哈尔滨工程大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TP391.3;TP18
本文编号：2221659
[Abstract]:With the development of Internet, search engine has become an important tool for people to search for information quickly. As one of the important economic foundations of search engine, keyword advertisement meets the marketing needs of advertisers effectively. At the same time, it also brings huge profits to search engine providers. Keyword advertising auction has not only become one of the most successful applications of microeconomic theory on the Internet, but also promoted the research of the mechanism behind it by many scholars in the field of science, and has become a research hotspot in the field of electronic commerce. The transaction Agent Competition, or TAC, is sponsored by schools such as Carnegie Mellon University to simulate real market behavior. Applying the current theoretical research results of artificial intelligence to real transactions. TAC / AA is a virtual keyword advertising auction platform. Researchers can apply the research results to this platform to verify the effectiveness of the algorithm. This paper designs a Agent model based on TAC/AA platform, which can formulate continuous optimal bidding strategy, and proposes dynamic and diverse elite PSO algorithm, which effectively solves the problem of local convergence of optimization problem in Agent model. Firstly, this paper analyzes the research status of keyword auction at home and abroad, introduces the design idea of TAC platform and the rules of competition, and then studies the theory of keyword auction and the related theories of evolutionary algorithm. Aiming at the keyword auction competition of TAC platform, this paper designs the TAC-HEU-AA (THA) Agent model, analyzes the efficiency of the algorithm of the predictor, discusses the necessity of each module through experiments, and compares the THA model with the Agent participating in the TAC/AA finals. The validity of the model is verified and the advantages and disadvantages of the model are analyzed. Aiming at the multi-selection knapsack problem of optimizer in THA model (MCKP,Multi-Choice Knapsack Problem), a new particle swarm optimization algorithm (DME-PSO) based on multiple elitist selection strategies is proposed. The movement trends of three kinds of particles and the motion behavior of four kinds of particles are defined. The particles are selected according to their motion behavior to maintain the diversity of the population. In the process of variation, the phenomenon of elite saturation of population is described, and the disturbance is added to the population in the state of elite saturation. In the experiment, DME-PSO algorithm is compared with greedy algorithm, multi-population genetic algorithm and particle swarm optimization algorithm with Gao Si disturbance. The experimental results show that the DME-PSO algorithm performs well when the number of items increases, thus solving the problem of local convergence of combinatorial optimization better.
【学位授予单位】：哈尔滨工程大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TP391.3;TP18
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