基于集成特征选择的点击欺诈检测方法

网络在线广告中以套取广告费为目的的点击欺诈已经严重影响了网络广告的稳定发展。从FDMA2012竞赛提供的欺诈发布商检测的真实数据集出发，针对冗余特征会降低训练效率以及不平衡数据会使决策边界发生偏倚的问题，提出了一种基于集成特征选择的网络在线广告点击欺诈检测方法。采用Bagging方法和合成少数类过采样技术（Synthetic Minority Oversampling Technique，SMOTE）相结合的方法将多数的正常点击广告发布商样本与少数的欺诈点击广告发布商样本构造为多个袋装子集，利用基于相关性度量的特征选择算法对每个袋装子集中筛选出特征子集，设置阈值得到特征合集，利用随机森林算法构建点击欺诈检测模型。实验结果表明该方法能够有效识别出实施欺诈点击行为的非法发布商，达到网络在线广告中点击欺诈检测的要求。     

A framework for intermediated online targeted advertising with banner ranking mechanism

Reinforced by the fast growth of electronic commerce, even during the current global economic downturn, intermediated online targeted advertising (IOTA) has emerged as a promising electronic business model empowered by the Web 2.0 principle. IOTA maximizes the profit of online targeted advertising services by displaying the proper banner contents to certain types of Web users in real time in order to increase the click-through rate (CTR). However, due to severe competition in the online advertising market, the principles and algorithms of IOTA remain highly confidential. This paper is intended to unveil the nature of IOTA. We propose an IOTA service system framework and present its implementation scheme. Specifically, we address the advertisement allocation problem, using an advertisement ranking mechanism and considering the ads impression quota and the time-of-day (TOD) effect. Simulation results show that advertisement ranking in a subset of clusters that actively estimates the quota situation is feasible and efficient.     

基于函数截获的游戏内置广告集成技术

伴随互联网的普及,互联网广告得到快速发展,游戏内置广告已成为一种新兴的互联网广告形式。由于玩家数量庞大,网络游戏的媒体价值不断增长,与其相关的广告集成技术也获得一定发展。现有游戏内置广告技术采用SDK的集成方式,需要大量修改网络游戏源代码,广告逻辑与游戏逻辑耦合度高,难以实现复杂的交互式广告。提出一种基于函数截获的游戏内置广告集成技术,克服现有SDK广告集成技术的不足。该技术截获并替换三维图形绘制函数,动态执行脚本[11]程序,在不修改游戏客户端源代码的情况下,集成图像、视频、三维物体和动态交互式点击广告。实验结果表明,应用该技术在网络游戏场景中集成广告,不影响游戏运行的流畅性,游戏画面仍保持较高的刷新帧率。     

Targeted interest-driven advertising in cities using Twitter

Targeted advertising is a key characteristic of online as well as traditional-media marketing. However it is very limited in outdoor advertising, that is, performing campaigns by means of billboards in public places. The reason is the lack of information about the interests of the particular passersby, except at very imprecise and aggregate demographic or traffic estimates. In this work we propose a methodology for performing targeted outdoor advertising by leveraging the use of social media. In particular, we use the Twitter social network to gather information about users’ degree of interest in given advertising categories and about the common routes that they follow, characterizing in this way each zone in a given city. Then we use our characterization for recommending physical locations for advertising. Given an advertisement category, we estimate the most promising areas to be selected for the placement of an ad that can maximize its targeted effectiveness. We show that our approach is able to select advertising locations better with respect to a baseline reflecting a current ad-placement policy. To the best of our knowledge this is the first work on offline advertising in urban areas making use of (publicly available) data from social networks.     

Modeling the clickstream across multiple online advertising channels using a binary logit with Bayesian mixture of normals

The evaluation of online marketing activities using standalone metrics does not explain the development of consumer behavior over time, although it is of primary importance to allocate and optimize financial resources among multiple advertising channels. We develop a binary logit model with a Bayesian mixture approach to demonstrate consumer clickstreams across multiple online advertising channels. Therefore, a detailed user-level dataset from a large financial service provider is analyzed. We find both differences in the effects of repeated advertisement exposure across multiple types of display advertising as well as positive effects of interaction between display and paid search advertising influencing consumer click probabilities. We identify two consumer types with different levels of susceptibility to online advertising (resistant vs. susceptible consumers) and show that the knowledge of consumers individual click probabilities can support companies in managing display advertising campaigns.     

Clustering Text Data Streams

Clustering text data streams is an important issue in data mining community and has a number of applica- tions such as news group filtering,text crawling,document organization and topic detection and tracing etc.However, most methods axe similaxity-based approaches and only use the TF*IDF scheme to represent the semantics of text data and often lead to poor clustering quality.Recently,researchers argue that semantic smoothing model is more efficient than the existing TF*IDF scheme for improving text clus...     

Hybrid genetic algorithm and augmented neural network application for solving the online advertisement scheduling problem with contextual targeting

Worldwide growth of the online community continues to push the popularity of internet marketing. Fueled by this trend, the online advertising industry is experiencing unprecedented revenue growth. One of the most important drivers of this revenue is banner advertising, which has long been a staple of the online advertising industry. Previous research has introduced quantitative models and solution approaches for the challenging basic scheduling optimization problem. We extend this work by incorporating the most common and popular trend in the in the industry, online advertisement targeting. In addition, motivated by the NP-hard nature of the resulting problem, we propose and test several heuristic and metaheuristic based solution techniques for the proposed problem.     

基于聚类分析的微博广告发布者识别

微博空间存在大量的广告内容,这些信息严重影响着普通用户的用户体验和相关的研究工作。现有研究多使用支持向量机（SVM）或随机森林等分类算法对广告微博进行处理,然而分类方法中人工标注大数据量训练集存在困难,因此提出基于聚类分析的微博广告发布者识别方法：对于用户维度,针对微博广告发布者通过发布大量普通微博来稀释其广告内容的现象,提出核心微博的概念,通过提取核心微博主题及其对应的微博序列,计算用户特征和对应微博的文本特征,并使用聚类算法对特征进行聚类,从而识别微博广告发布者。实验结果显示,所提方法准确率为92%,召回率为97%,F值为95%,证明所提方法在广告内容被人为稀释的情况下能准确地识别微博广告发布者,可以为微博垃圾信息识别、清理等工作提供理论支持和实用方法。     

当代影视广告创意语言探析

当代广告与生活已紧密相连,广告已经渗透到我们生活的方方面面。影视广告即电影、电视广告影片,作为广告重要组成部分的影视广告,其广泛用于企业形象宣传、产品推广,具有着广泛的社会接受度。我们主要把影视创意方式分为亲情式表达,意境式表达,幽默式表达,形象代言人直叙型,故作悬念式表达手法等多种表达方式。将多种方式搭配一起使用,将取得事半功倍的效果。     

Ontological analysis of web surf history to maximize the click-through probability of web advertisements

Due to an enormous influx of capital over the past decade, the online advertising industry has become extremely robust and competitive. The difference between success and failure in such a competitive market often rests in the ability to deliver advertisements that are closely in line with a user's interests. In this work, we propose and test a new online advertisement targeting technique which adapts and utilizes several powerful and well tested information retrieval and lexical techniques to develop an estimate of a user's affinity for particular products and services based on an analysis of a user's web surfing behavior. This new online ad targeting technique performs extremely well in our empirical tests.     

基于改进密度峰值聚类算法的轨迹行为分析

为了深入挖掘校园无线网络轨迹行为数据信息,采用基于密度的聚类方法对校园内用户的轨迹行为进行特征聚类。由于基于密度的聚类算法通常采用距离作为相似性度量方式,为了有效衔接此类聚类算法,先将用户相似度矩阵通过转换函数转变为距离矩阵。引入离群点检测算法,将离群点检测算法与聚类算法相结合,减少参数的输入个数,增加聚类的聚合程度。改进后的聚类算法可以有效检测出数据轨迹的异常,帮助高校通过对学生上网记录的处理找到浏览信息与大部分同学不一致的人,缩小目标范围,进行有针对性的处理。通过定性分析和实验对比验证,确定两种基于离群点检测的共享最近邻的快速搜索密度峰值聚类适用于校园无线网络行为轨迹相似度矩阵的处理,邓恩指数等聚类内部指标及整体性能优于同类算法。     

Optimizing direct response in Internet display advertising

Internet display advertising has grown into a multi-billion dollar a year global industry and direct response campaigns account for about three-quarters of all Internet display advertising. In such campaigns, advertisers reach out to a target audience via some form of a visual advertisement (hereinafter also called “ad”) to maximize short-term sales revenue. In this study, we formulate an advertiser’s revenue maximization problem in direct response Internet display advertisement campaigns as a mixed integer program via piecewise linear approximation of the revenue function. A novelty of our approach is that ad location and content issues are explicitly incorporated in the optimization model. Computational experiments on a large-scale actual campaign indicate that adopting the optimal media schedule can significantly increase advertising revenues without any budget changes, and reasonably sized instances of the problem can be solved within short execution times.     

A kernel‐based core growing clustering method

In this paper, a novel clustering method in the kernel space is proposed. It effectively integrates several existing algorithms to become an iterative clustering scheme, which can handle clusters with arbitrary shapes. In our proposed approach, a reasonable initial core for each of the cluster is estimated. This allows us to adopt a cluster growing technique, and the growing cores offer partial hints on the cluster association. Consequently, the methods used for classification, such as support vector machines (SVMs), can be useful in our approach. To obtain initial clusters effectively, the notion of the incomplete Cholesky decomposition is adopted so that the fuzzy c‐means (FCM) can be used to partition the data in a kernel defined‐like space. Then a one‐class and a multiclass soft margin SVMs are adopted to detect the data within the main distributions (the cores) of the clusters and to repartition the data into new clusters iteratively. The structure of the data set is explored by pruning the data in the low‐density region of the clusters. Then data are gradually added back to the main distributions to assure exact cluster boundaries. Unlike the ordinary SVM algorithm, whose performance relies heavily on the kernel parameters given by the user, the parameters are estimated from the data set naturally in our approach. The experimental evaluations on two synthetic data sets and four University of California Irvine real data benchmarks indicate that the proposed algorithms outperform several popular clustering algorithms, such as FCM, support vector clustering (SVC), hierarchical clustering (HC), self‐organizing maps (SOM), and non‐Euclidean norm fuzzy c‐means (NEFCM). © 2009 Wiley Periodicals, Inc.4     

Advertisement clicking prediction by using multiple criteria mathematical programming

In online advertisement industry, it is important to predict potentially profitable users who will click target ads (i.e., Behavioral targeting). The task selects the potential users that are likely to click the ads by analyzing user’s clicking/web browsing information and displaying the most relevant ads to them. This paper proposes four multiple criteria mathematical programming models for advertisement clicking problems. First two are multi-criteria linear regression (MCLR) and kernel-based multiple criteria regression (KMCR) algorithms for click-through rate (CTR) prediction. The second two are multi-criteria linear programming (MCLP) and kernel-based multiple criteria programming (KMCP) algorithms, which are used to predict ads clicking events, such as identifying clicked ads in a set of ads. Using the experimental datasets from KDD Cup 2012, the paper first conducts a comparison of the proposed MCLR and KMCR with the methods of support vector regression (SVR) and logistic regression (LR), which shows that both MCLR and KMCR are good alternatives. Then the paper further studies the performance between the proposed MCLP and KMCP algorithms with known algorithms, including support vector machines (SVM), LR, radial basis function network (RBFN), k-nearest neighbor algorithm (KNN) and Naïve Bayes (NB) in both prediction and selection processes. The studies show that the MCLP and KMCP models have better performance stability and can be used to effectively handle behavioral targeting application for online advertisement problems.     

浅议户外广告与城市建设的和谐发展

随着世界各国市场经济的不断发展,人们物质、精神生活水平的不断提高,户外广告在广告行业中扮演着举足轻重的角色。遗憾的是户外广告与城市建设各自为阵,不能够有机的和谐发展,这是目前制约我国户外广告发展的瓶颈。本文较为系统的提出了户外广告是城市环境建设和谐发展的一个重要组成部分,明确提出了只有讲究和谐设计、广告内容与环境相统一、符合美学原则的户外广告才是未来城市户外广告发展的趋势。     

网络广告的受众分析

网络广告的受众主要集中在以追逐新鲜事物为乐趣的中青年一代,他们的消费心理主要表现在享受、猎奇、逆反、感性等几个方面,充分了解网络广告受众的状态,才能在做广告时做到有的放矢。     

基于在线聚类和检测成本的移动自组网异常检测

移动自组网具有无线信道、动态拓扑、缺乏基础设施和节点资源受限等特点,更易受到安全威胁,且无法部署复杂的安全协议和算法.为了有效检测移动自组网中的异常访问行为,提出了一种基于在线聚类和检测成本的异常检测方案TCDC.TCDC先在单个节点内对访问行为进行在线聚类和处理,然后在不同节点间通过基于检测成本的协同检测进一步确认访问行为.仿真实验表明,该异常检测方案能够有效地检测移动自组网中的异常行为,且消耗资源较少.     

Consumer responses to promoted tweets sent by brands and political parties

This study aims to understand how consumers respond to social media advertising (SMA) by focusing on promoted tweets sent by brands and political parties, and examines persuasion knowledge as underlying mechanism of these responses. Two online experiments with between-subjects designs, comparing the effects of SMA (promoted vs. non-promoted tweet) and the source of the tweet (political party vs. brand), were conducted. Study 1 showed that consumers rarely notice it when a tweet is promoted. Study 2 demonstrated that when a promoted tweet was sent by a political party, the recipient's recognition that the tweet was a form of advertisement (i.e., activated persuasion knowledge) reduced online behavioral intention, increased skepticism, and negatively affected source trustworthiness and attitudes. This effect was not present for brands. Although research has shown that social media can be an important platform to engage audiences, this study is the first to study the mechanisms underlying the effects of SMA, and whether there are any boundary conditions to these effects. These findings suggest that political parties should be cautious in their use of social media advertising as it can evoke negative responses.     

网络环境下的广告投放Vague决策模型研究

提出了基于 Vague链接倾向图的广告决策模型 .在分析网络广告有关特性的基础上 ,定义了 Vague关系的自反、对称与传递特性 ,特别强调了对于 Vague链接倾向图中的传递特性的应用 .通过计算广告与 Web链接网的总体匹配度 ,分析了广告在用户中所产生的不同影响程度 ,同时还给出了关于广告投放的 Vague决策算法 ,实现了在广告预算经费一定的前提下 ,如何投放广告于不同的 Web站点以获取最大的收益     

Online cluster validity indices for performance monitoring of streaming data clustering

Cluster analysis is used to explore structure in unlabeled batch data sets in a wide range of applications. An important part of cluster analysis is validating the quality of computationally obtained clusters. A large number of different internal indices have been developed for validation in the offline setting. However, this concept cannot be directly extended to the online setting because streaming algorithms do not retain the data, nor maintain a partition of it, both needed by batch cluster validity indices. In this paper, we develop two incremental versions (with and without forgetting factors) of the Xie-Beni and Davies-Bouldin validity indices, and use them to monitor and control two streaming clustering algorithms (sk-means and online ellipsoidal clustering), In this context, our new incremental validity indices are more accurately viewed as performance monitoring functions. We also show that incremental cluster validity indices can send a distress signal to online monitors when evolving structure leads an algorithm astray. Our numerical examples indicate that the incremental Xie-Beni index with a forgetting factor is superior to the other three indices tested.