基于淘宝直通车的竞价决策系统的研究与设计

电子商务蓬勃发展的大环境下,广告主具有强烈的电商广告投放意愿,显然他们并未达到电商广告的核心业务SEM （搜索引擎营销优化）的专业要求。所以广告主希望借助第三方工具来进行搜索引擎广告投放的一站式服务来满足其业务需求。基于此,本文将提供一整套的竞价词托管式服务的解决方案。以淘宝直通车这一全新的搜索竞价模式作为研究对象,从语义抽取、关键词扩展、竞价词生成、模型化出价、广告效果正向反馈监控模型几方面进行分析和统计,为直通车广告主提供最优投放策略整体解决方案。第一阶段针对商品信息进行数据挖掘,实现关键词推荐引擎。第二阶段实现投放优化模块,实施定价策略,建立的点击量与PPC（“平均点击花费”）模型,实现在预算资金的约束下对不同竞价组合进行ROI（投入产出比）最大化的投资决策。以实际效果改善直通车竞价搜索用户体验。     

网络广告关键词选择优化

搜索引擎广告系统的核心任务是针对用户提交的每个查询,决定以何种顺序显示哪些广告。这是广告商和搜索引擎之间的一个博弈过程：搜索引擎需要准确地估算广告的点击率,提高广告收益、广告商的满意度及用户的满意度;广告商则需要猜测搜索引擎的排序模型、竞价广告栏并依据广告效果调整广告策略以及执行其他的搜索引擎优化动作。探讨了推测CPC模式下搜索引擎广告排序模型的一般方法,并据此指导广告商的关键词选择策略。     

大数据,数字营销的灵魂

正大数据应用作为一个高渗透力的行业,正在与广告业发生深度的融合,从预算分配到投放调整,再到投放后评估,大数据已经开始打破原有的思维,彻底改变了整个广告行业。在数字广告受到越来越多广告主青睐的同时,广告投放的真实性和精准性、广告效果评估等关键因素成为了广告主关注的焦点。站在公正的立场上扮演裁判的角色,通过尖端的技术挖掘数据并进行深入分析和研究,并利用数据的价值来实     

网上交易平台中关键字推广和广告研究与应用

在电子商务市场上选择正确的单词是很重要否则很容易迷失在巨大的平台。在平台上需要广告自己的产品的话正确的关键字起着重要的作用。每个单词的句子提供某些索引擎页面中的位置。因此,对于可见光和网页中的位置这个必要知道竞争对手卖的公共关键字。本文提出一种专门针对电子商务领域关键词竞价的关键词选取算法。此类研究具有很强的实际应用价值,电子商务的广告主在进行广告推广时,由于关键字竞价机制的复杂性、不完全信息特征以及广告主自身对关键词缺乏深刻的理解,导致关键字竞价广告的投标策略失误。本文重新定义关键词选取算法,适应电子商务领域文本特性。通过商品标题文本,获取推荐关键词。整个过程首先建立分层次行业相关词表,然后根据词表完成关键词推荐,推荐分为两个步骤：种子关键词抽取;推荐关键词扩展。     

搜索营销标准“中国造”

持续了一年的金融危机,让广告主在市场投入面前异常谨慎,而这样的局面也刺激了以高ROI为特点的搜索营销市场的繁荣。 艾瑞最新数据显示,在选择网络广告的投放媒体时,有57．3％的广告主把搜索引擎类网站作为首选。市场的高认可度,意味着搜索营销已经逐渐成为中国网络营销的主流阵地。     

派瑞威行——精准营销行业的领跑者

正经过近几年的培育,大数据和精准广告在今年有了飞跃式的发展,越来越多的广告主正全面参与到整个广告程序化产业生态中,数字广告领域基本上所有的公司都推出了自己的精准广告产品,精准营销市场已然出现了井喷的态势。所谓精准营销,也就是以更为准确的受众覆盖实现最佳的营销效果。广告主最看重的依然是效果这个最看得见的指标,永远只会把大部分预算放在现阶段投入产出比最高的广告平台上。而也     

基于Ajax技术的文内关键词广告的设计与实现

论述了网站文章正文中关键词广告的作用和优点,对比了其与搜索引擎关键词广告和文本链接广告的区别,利用AjaSx 技术,提出了在正文中实现关键词广告系统的设计,并给出其关键的实现.     

考虑时序约束的多智能体协同任务分配

研究多智能体系统的多目标多任务分配问题, 考虑任务之间的时序关系, 建立分布式任务分配模型. 扩展了一致性包算法(CBBA), 按优先级将目标任务归入不同层级, 各智能体在构建任务包和任务路径时, 只将分配过高阶段任务的目标添加至相应的任务包和任务路径中, 从而保证目标任务时序约束的同时, 保持了CBBA算法的特性. 与多任务分配问题经典算法的比对实验表明, 所提出的改进算法求解结果稳定可靠, 运行时间优于经典算法.

     

博客

互联网广告市场最不平常的一年；英女王YouTube上祝圣诞；有六分之一的搜索引擎广告主是B2B网站的付费会员。     

基于二级向量描述的搜索引擎个性化服务模型

本文介绍了一种搜索引擎个性化服务模型。用二级向量进行文本特征提取和用户兴趣建模,关键词向量能快速定位用户的兴趣领域,而扩展词向量能准确反映用户在该领域上的兴趣偏好。当用户提交关键词时,本系统根据学习到的用户兴趣描述模型计算词间相关度,自动增加几个个性化扩展词提交给搜索引擎,实现不同用户键入相同关键词能返回不同信息的目的。实验结果充分表明本系统应用于搜索引擎个性化服务领域的有效性和实用性。     

An analysis of the importance of the long tail in search engine marketing

Search engine marketing is currently the most popular form of online advertising. Many advertising agencies and bloggers claim that the success of search engine marketing is driven by the “long tail”, defined in this research as the many less popular keywords employed by users to search the Internet, and suggest that search engine marketing campaigns need to have hundreds or thousands of keywords to accommodate this phenomenon. We doubt this claim. Our data from three search engine marketing campaigns in two countries, which report the success of a total of 4908 keywords over 36 weeks, covering 10,104,015 searches and 492,735 clicks, show that the top 20% of all keywords attract on average 98.16% of all searches and generate 97.21% of all clicks. The use of the top 100 keywords in each campaign attracts on average 88.57% of all searches and 81.40% of all clicks. These results are fairly stable across a varying total number of keywords in use and suggest that the success of search engine marketing is driven by relatively few keywords. However, we also show that the set of the top 100 keywords changes over time. Hence, advertisers need to concentrate on finding the top 100 keywords, but they do not need to bother too much about the performance of keywords in the long tail.     

How to select search keywords for online advertising depending on consumer involvement: An empirical investigation

In paid search advertising on Internet, advertisers bid for specific keywords. Consumer involvement affects a consumer’s purchase intention. However, there has been no research on selecting keywords in paid search advertising depending on consumer involvement from the advertisers’ perspective. Our paper investigates the effects of several factors including impressions, click-through rate, conversion rate, the number of competitors and the quantity of online customer reviews on the performance of individual keywords. Our analysis shows that regardless of consumer involvement, the conversion rate and the number of reviews significantly affect to the performance of individual keywords but the click-through rate does not. In addition, the effects of impressions and the number of competitors partially exist depending on consumer involvement. This study provides managerial insights into paid search advertising and can give some assumptions in the theoretical modeling literature for allocating advertising budgets.     

An empirical test to forecast the sales rank of a keyword advertisement using a hierarchical Bayes model

Online advertising (ad) is a form of promotion that uses the Internet and World Wide Web for the expressed purpose of delivering marketing messages to attract customers. Not surprisingly, how to predict the effectiveness of online advertising has gained lots of research attention. This study introduces the hierarchical Bayesian analysis to the online advertising effect model involving competition with other products. It developed a competition model with a time-decaying effect that is applicable for the sales-rank data in the online marketplace. The proposed model formalizing the hierarchical structure has performed better than the reduced model without having random effect components. It captures the heterogeneous advertising responses across the products as well as search keywords. Our results have implications for online advertising effect measurement, and may help guide advertisers in decision-making.     

搜索引擎自动性能评价系统构建的研究

检索系统的评价问题一直是信息领域最核心的问题之一。公开有效的搜索引擎质量评估可以指导搜索引擎用户挑选最有利于获取信息的手段,促进搜索引擎服务提供者与研究人员不断尝试新技术,为搜索引擎广告商挑选最有效的广告投放手段。传统的搜索引擎评价系统已经不能满足实际需要,提高搜索引擎评价方法的自动化程度势在必行。本文提出利用用户群体行为分析来进行搜索引擎自动性能评价,通过利用用户点击行为中蕴含的信息对检索结果进行相关性的评价,最终达到对各种不同搜索引擎进行性能评价的目的。     

Exploiting the Community Structure of Fraudulent Keywords for Fraud Detection in Web Search

Internet users heavily rely on web search engines for their intended information.The major revenue of search engines is advertisements (or ads).However,the search advertising suffers from fraud.Fraudsters generate fake traffic which does not reach the intended audience,and increases the cost of the advertisers.Therefore,it is critical to detect fraud in web search.Previous studies solve this problem through fraudster detection (especially bots) by leveraging fraudsters' unique behaviors.However,they may fail to detect new means of fraud,such as crowdsourcing fraud,since crowd workers behave in part like normal users.To this end,this paper proposes an approach to detecting fraud in web search from the perspective of fraudulent keywords.We begin by using a unique dataset of 150 million web search logs to examine the discriminating features of fraudulent keywords.Specifically,we model the temporal correlation of fraudulent keywords as a graph,which reveals a very well-connected community structure.Next,we design DFW (detection of fraudulent keywords) that mines the temporal correlations between candidate fraudulent keywords and a given list of seeds.In particular,DFW leverages several refinements to filter out non-fraudulent keywords that co-occur with seeds occasionally.The evaluation using the search logs shows that DFW achieves high fraud detection precision (99％) and accuracy (93％).A further analysis reveals several typical temporal evolution patterns of fraudulent keywords and the co-existence of both bots and crowd workers as frandsters for web search fraud.     

搜索引擎自动性能评价系统构建的研究

检索系统的评价问题一直是信息领域最核心的问题之一。公开有效的搜索引擎质量评估可以指导搜索引擎用户挑选最有利于获取信息的手段，促进搜索引擎服务提供者与研究人员不断尝试新技术，为搜索引擎广告商挑选最有效的广告投放手段。传统的搜索引擎评价系统已经不能满足实际需要，提高搜索引擎评价方法的自动化程度势在必行。本文提出利用用户群体行为分析来进行搜索引擎自动性能评价，通过利用用户点击行为中蕴含的信息对检索结果进行相关性的评价，最终达到对各种不同搜索引擎进行性能评价的目的。     

Online keyword based advertising: Impact of ad impressions on own-channel and cross-channel click-through rates

Keyword-based ads are becoming the dominant form of advertising online as they enable customization and tailoring of messages relevant to potential consumers. Two prominent channels within this sphere are the search channel and the content channel. We empirically examine the interaction between these two channels. Our results indicate significant cannibalization across the two channels as well as significant diminishing returns to impressions within each channel. This suggests that under certain conditions both channels may need to be used to optimize returns to advertising both for advertisers and service providers such as Google. Our game theoretic analysis which builds upon our empirical findings reveals that for intermediate budget values it is optimal to use both channels whereas for very low (very high) budget values it is optimal to use only the content (search) channel. Further as budget increases the advertiser should offer more for ads displayed on the search channel to optimally incentivize the service provider.     

Co-evolution-based mechanism design for sponsored search advertising

Sponsored search advertising (SSA), the primary revenue source of Web search engine companies, has become the dominant form of online advertising. Search engine companies, such as Google and Baidu, are naturally interested in SSA mechanism design with the aim to improve the overall effectiveness and profitability of SSA ecosystems. Due to model intractability, however, traditional game theory and mechanism design frameworks provide only limited help as to the design and evaluation of practical SSA mechanisms. In this paper, we propose a niche-based co-evolutionary simulation approach, aiming at computationally evaluating SSA auction mechanisms based on advertisers’ equilibrium bidding behavior generated through co-evolution of their bidding strategies. Using this approach, we evaluate and compare key performance measures of several practical SSA auction mechanisms, including the generalized first and second price auction, the Vickrey–Clarke–Groves mechanism, and a novel hybrid mechanism adopted by sogou.com, a major search engine in China.