摘要
随着互联网的迅速发展,网络广告也得到了快速的发展。与传统媒体广告相比,网络广告能通过多媒体方式,全天候、全球性的展示,具有成本低、互动性强的特点。网络广告越来越受到广告商的青睐,并成为许多学者研究的对象。
近几年,网络广告不论在商业应用还是研究领域都获得了极大的发展。然而随着网络广告爆炸式的投放,横幅广告点击率不断下降,网络广告的发展前景受到了诸多质疑。为了提高网络广告的点击率,为每个用户提供个性化的广告信息是当前业界与学界的一个研究焦点。
RSS广告是一种新型的网络广告形式,针对RSS广告的个性化推荐问题,本文对RSS个性化网络广告系统进行了探讨,针对其中的关键算法进行了有益的分析与研究,包括分类算法、关联规则挖掘算法和协同过滤算法。
首先利用关联规则挖掘算法分析用户的浏览行为,并提出基于频繁1-项子树(FS-tree)的频繁模式挖掘算法。在线RSS阅读器拥有大量的注册用户,他们产生的事务数据集非常庞大,如果使用FP-growth算法进行关联规则的挖掘,需要占用大量内存空间。针对这个问题,提出基于频繁1-项子树(FS-tree)的频繁模式挖掘算法。该算法通过构建频繁1-项子树,使FP-tree的最大深度和最大宽度得以分离。实验结果表明,该算法占用内存小,执行效率高,能有效的进行关联规则的挖掘。
接下来利用协同过滤算法分析用户的订阅行为,并提出基于项目分类的协同过滤算法。由于在RSS在线阅读器网站上,用户订阅的Feed只占所有Feed的极少数,用户数与Feed数的急剧增加将导致用户评分矩阵变得极端稀疏,造成两个用户共同评分过的项非常稀少,因此通过协同过滤算法计算邻居用户不准确,推荐质量急剧下降。针对该问题,提出基于项目分类的协同过滤推荐算法。该算法利用系统已经产生的Feed分类结果,计算类中各个项之间的相似性,然后再通过加权的方法对未评分项进行预测评分,从而有效降低了数据的稀疏性。实验结果表明,基于项目分类的协同过滤推荐算法可以有效解决在用户评分数据极端稀疏的情况下,传统相似性度量方法存在的不足,显著提高推荐系统的推荐质量。
提出智能广告推荐策略。一个有效的广告推荐系统首先应该能根据各种情况调整广告推荐集的输出,并能对广告商的一些要求设置广告过滤规则。针对这个问题,本文设置了若干过滤规则,并提出一个混合推荐策略,使系统能根据问题背景和实际情况产生不同的广告推荐集。如果是一个老用户,将调用协同过滤推荐算法产生推荐集,使用户在登录的初始阶段就能获得个性化的RSS广告。之后,随着用户访问的深入,当用户当前的浏览序列到达一定长度值L时,将触发关联规则推荐算法,否则采用分类推荐算法产生广告集。采用混合广告推荐策略,可以有效解决新用户、新项目问题,由于协同过滤算法和关联规则挖掘算法都是离线完成的,系统具有较好的实时性。
提出基于决策树的广告客户流失模型。客户流失管理是许多行业关注的一个重要问题,广告商作为RSS在线阅读器的主要收入来源,应该对他们的流失予以高度关注。针对这个问题,本文以CRM中的RFM模型为基础,根据广告业的实际情况,提出广告客户流失模型,并利用决策树算法对该模型进行了分析。
With the rapid development of the Internet, Internet Advertising has been rapid development. Compared with traditional media advertising, Internet Advertising through multi-media, all-weather(smy), global exposure, with low-cost, highly interactive features. Internet Advertising favored by more and more advertisers, and becomed the research subject of many scholars.
In recent years, both in commercial application and research field, Internet Advertising has been a great development. However, with the explosion of Internet Advertising delivery, the click through rate(CTR) of the banner falling. The prospect of Internet advertising has been questioned. In order to increase the CTR of Internet Advertising, providing personalized information for each user has been a research focus in the ad industry and the academic currently.
An algorithm for mining frequent patterns based on FS-tree (1 item frequent sub-tree). Online RSS readers have a large number of registered users, it leads to very large data sets. So if use FP-growth algorithm mining association rules for them, it need to occupy a lot of memory space. To solve this issue, the algorithm for mining frequent patterns based on FS-tree has been put forward. The algorithm enables the FP-tree the greatest depth and width to the largest separation, through constructing FS-tree. Experimental results show that the algorithm occupys small memory, the implementation of efficient and effective conduct of the association Mining Rules.
An algorithm based item classification collaborative filtering. On the online RSS reader site, the number of feeds by users subscribed accounts for only a very small number. So the number of users and Feeds sharply increase will lead to score matrix become extremely sparse, resulting the very scarce items with two users of the common score. So it does not accurately calculate the neighbors similayity by collaborative filtering algorithms, and lead to recommended quality a sharp decline. Against the problem, it put forward the recommended algorithm based item classification collaborative filtering. The algorithm uses the results of Feeds classification through system, to calculate the similarity between the various items, and then through the method of weighted, to forecast the score of items which did not been score, thus effectively reducing the sparsity of data. The results showed that, the algorithms can effectively resolve the defects of the traditional similarity measurement methods, when the score in the sparse data, and significantly improve the recommend system's quality.
The strategy of smart advertising recommended. An effective ad recommendation system should have the capacity that adjust the output of sets of ads recommendation, according to variety factors, and set ads filter rules for the demand of advertisers. So, the paper set a number of filtering rules, and put forward a mixed recommendation strategy. It can enable the system generate different sets of ads recommendation according to background of issues and the actual situation. It will call collaborative filtering algorithms to get sets of ads recommendation for registered users. Then registered users can get the personalized RSS ads when they login. With access to in-depth, when the length with current view sequence of users reach a value, it will trigger association rules recommended algorithm, otherwise trigger classification recommended algorithms to get sets of ads. The mixed ads recommendation strategy efficient solve the issues of new users and new projects. Both collaborative filtering algorithms and association rule mining algorithms are completed offline, so the system has better real-time.
Based on the decision tree model of the loss of advertisers. The management of the loss of customers is many industries concerned about an important issue. Advertisers are the main source of income of online RSS reader, it should be great concern to the loss of them. To address this issue, based on RFM model of CRM, this paper put forward the models of the loss of advertisers, according to the actual situation in the advertising industry, and using decision tree algorithm to analsis the model.
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