基于相似模式聚类的电子商务网站个性化推荐系统研究

保证个性化推荐系统产生高质量的推荐结果的重要因素是:系统必须要确定访问者在访问行为的相似程度,从而能预测访问者的访问和购买兴趣。实现此功能的关键技术是计算访问者对象在整个或者部分属性空间的相似距离,从而得到访问行为的相似程度。该文首先分析了目前在推荐系统中常用的用于计算访问行为相似程度的距离函数,发现它们是测定访问者对象在所有测试属性空间上的平均测定,而在属性集的子维空间上的相似模式并没有有效地挖掘出来。然后提出一种新的基于相似模式聚类算法的电子商务个性化推荐系统,综合考虑可供挖掘的数据源(如:网站内容,网站的超链接结构,顾客访问网站的行为,以及商业的实际购买情况,顾客的身份数据等)获取用户访问电子商务网站的访问页面序列,构建较高购买者的顾客行为的矩阵模型,高效地得到访问者对象在整个或者部分属性空间的相似访问行为,然后通过挖掘潜在购买者与较高购买者的相似模式特征,帮助顾客发现他所希望购买的产品信息,用于提高实际购买量,实验数据表明,该系统高效并可广泛使用。     

基于客户聚类的商品推荐方法的研究

文中给出了一种新的数据源的获取方法,使用Web2.0技术直接从客户浏览行为中获取需要的数据,避免了传统Web使用数据挖掘时日志数据预处理时的大量繁杂工作,减少了噪声数据,提高了数据准确性。根据所获数据建立用户-商品矩阵,计算此矩阵的欧氏距离,在此基础上使用聚类算法将客户进行聚类,根据聚类结果对新来的客户进行有目的的商品推荐,并对聚类结果进行跟踪评价。目的是为了提高电子商务网站的个性化服务。     

基于客户聚类的商品推荐方法的研究

文中给出了一种新的数据源的获取方法,使用Web2.0技术直接从客户浏览行为中获取需要的数据,避免了传统Web使用数据挖掘时日志数据预处理时的大量繁杂工作,减少了噪声数据,提高了数据准确性.根据所获数据建立用户-商品矩阵,计算此矩阵的欧氏距离,在此基础上使用聚类算法将客户进行聚类,根据聚类结果对新来的客户进行有目的的商品推荐,并对聚类结果进行跟踪评价.目的是为了提高电子商务网站的个性化服务.     

基于偏好的客户模糊聚类方法

在电子商务环境下,如何按照顾客的购买兴趣进行聚类分析并为其提供个性化服务,是电子商务应用中研究的热点课题之一时.顾客的浏览行为及兴趣进行了研究,提出了利用偏好度的方法来度量顾客的兴趣度,在此基础上给出了基于偏好的客户群聚类算法.在该算法中,依据Web日志数据计算顾客偏好度,建立偏好度矩阵,再利用模糊聚类方法对顾客进行聚类.并用实例说明了具体的聚类过程.     

基于聚类挖掘技术在电子商务网站中的应用

数据挖掘技术是一种新的信息处理技术。其目的是从海量数据中抽取潜在的,有价值的数据规律或数据模型。通过数据挖掘技术对电子商务网站数据的分析处理,结合客户关系管理策略,建立反映客户个性特征的客户特征模型,建立动态适应性的服务机制,有效地为不同类型的客户进行个性化服务。该文主要将聚类技术应用到电子商务网站,通过建立商品数据库,利用频繁项集的方法得到客户聚类向量,计算出客户的相异度矩阵,用聚类技术实现客户的分类。     

基于ART算法的电子商务个性化聚类模型的设计与实现

本文针对个性化电子商务网站建设中出现的难于有效发现用户行为特征问题,提出一种基于ART神经网络自适应谐振算法的个性矢量聚类模型。该聚类模型由两个智能子系统和三个逻辑控制单元组成,采用二值输入模式,具备很强的自适应性。模型实现是在C＋＋语言平台上进行的。在模型程序设计中,采用衍生类方式的构造子系统单元,通过控制对话、数据共享建立系统单元之间的联系。该模型可以有效挖掘网络用户行为典型个体特征,用于指导电子商务网站资源的组织和再分配。     

一种基于Web数据挖掘的电子商务推荐系统

在对Web数据挖掘技术和电子商务推荐系统进行研究生的基础上,设计和提出了一种基于Web数据挖掘的电子商务推荐系统.该系统根据电子商务网站的基本特征,设计了用户当前兴趣表示方法和推荐算法,由于结合了Web使用挖掘和Web内容挖掘为顾客提供个性化推荐服务,从而较大提高了系统的推荐精确度,在实际应用中取得了较好的推荐效果.     

基于聚类协作过滤的个性推荐系统的实现

根据目前电子商务网站中商品个性化推荐的现状.本文提出时不同的商品可分别根据商品间的相似性和顾客间的相似性进行聚类和协作推荐.并具体给出基于聚类协作过滤的商品个性化推荐的流程、系统设计和系统实现.     

改进的Apriori算法在电子商务推荐系统中的应用

针对目前电子商务的推荐系统不能适用于中小电子商务网站,文章使用改进的Apriori算法对电子商务交易事务数据库中的数据进行挖掘,首先对不同类的事务数据库中的最小支持度和最小置信度阈值进行区分设置,寻找最优值;然后对事务数据库中的数据进行稀疏性设置,转换成稀疏性矩阵的形式,以加快算法的执行效率,并每次都对与候选集中无关的项进行删除,再扫描修剪后的稀疏性矩阵,这样进一步提高挖掘效率。最后通过以某中小洁具用品电子商务网站的交易数据为对象,给出详细的操作方法和实验结果。     

基于电商客户行为的电子商务数据质量评估模型研究

本文旨在为电子商务网站的质量评估提出一种新方法.通过在线活动评估客户的满意度,电商网站的标准视为评估系统的输入变量,考虑到消费者的行为可能被解释为模糊值的事实,这些标准的值是根据潜在用户使用该网站的在线行为隐式捕获的.所提出的电商数据评估模型是一种多准则程序,结合模糊逻辑,可以搜索有价值的动态信息,以自动评估电子商务网...     

A social recommender system using item asymmetric correlation

Recommender systems have been one of the most prominent information filtering techniques during the past decade. However, they suffer from two major problems, which degrade the accuracy of suggestions: data sparsity and cold start. The popularity of social networks shed light on a new generation of such systems, which is called social recommender system. These systems act promisingly in solving data sparsity and cold start issues. Given that social relationships are not available to every system, the implicit relationship between the items can be an adequate option to replace the constraints. In this paper, we explored the effect of combining the implicit relationships of the items and user-item matrix on the accuracy of recommendations. The new Item Asymmetric Correlation (IAC) method detects the implicit relationship between each pair of items by considering an asymmetric correlation among them. Two dataset types, the output of IAC and user-item matrix, are fused into a collaborative filtering recommender via Matrix Factorization (MF) technique. We apply the two mostly used mapping models in MF, Stochastic Gradient Descent and Alternating Least Square, to investigate their performances in the presence of sparse data. The experimental results of real datasets at four levels of sparsity demonstrate the better performance of our method comparing to the other commonly used approaches, especially in handling the sparse data.     

Bounded matrix factorization for recommender system

Matrix factorization has been widely utilized as a latent factor model for solving the recommender system problem using collaborative filtering. For a recommender system, all the ratings in the rating matrix are bounded within a pre-determined range. In this paper, we propose a new improved matrix factorization approach for such a rating matrix, called Bounded Matrix Factorization (BMF), which imposes a lower and an upper bound on every estimated missing element of the rating matrix. We present an efficient algorithm to solve BMF based on the block coordinate descent method. We show that our algorithm is scalable for large matrices with missing elements on multicore systems with low memory. We present substantial experimental results illustrating that the proposed method outperforms the state of the art algorithms for recommender system such as stochastic gradient descent, alternating least squares with regularization, SVD++ and Bias-SVD on real-world datasets such as Jester, Movielens, Book crossing, Online dating and Netflix.     

A Knowledge Base for the maintenance of knowledge extracted from web data

By applying web mining tools, significant patterns about the visitor behavior can be extracted from data originated in web sites. Supported by a domain expert, the patterns are validated or rejected and rules about how to use the patterns are created. This results in discovering new knowledge about the visitor behavior to the web site. But, due to frequent changes in the visitor’s interests, as well as in the web site itself, the discovered knowledge may become obsolete in a short period of time. In this paper, we introduce a Knowledge Base (KB), which consists of a database-type repository for maintaining the patterns, and rules, as an independent program that consults the pattern repository. Using the proposed architecture, an artificial system or a human user can consult the KB in order to improve the relation between the web site and its visitors. The proposed structure was tested using data from a Chilean virtual bank, which proved the effectiveness of our approach.     

Web挖掘在电子商务中的应用

如何从海量的Web数据中发现有用的知识是一个迫切需要研究的课题,因此,Web挖掘应运而生,成为一个全新的研究领域。Web挖掘就是从Web文档和Web活动中抽取潜在的有用模式和隐藏信息。随着电子商务的发展,Web挖掘进入了一个新的应用领域,介绍了Web挖掘技术在电子商务中的具体应用,运用Web挖掘技术对Web数据进行挖掘,了解客户的行为,从而调整站点结构、市场策略等,使电子商务活动具有针对性。     

Studying the feasibility of a recommender in a citizen web portal based on user modeling and clustering algorithms

This paper presents a methodology to estimate the future success of a collaborative recommender in a citizen web portal. This methodology consists of four stages, three of them are developed in this study. First of all, a user model, which takes into account some usual characteristics of web data, is developed to produce artificial data sets. These data sets are used to carry out a clustering algorithm comparison in the second stage of our approach. This comparison provides information about the suitability of each algorithm in different scenarios. The benchmarked clustering algorithms are the ones that are most commonly used in the literature: c-Means, Fuzzy c-Means, a set of hierarchical algorithms, Gaussian mixtures trained by the expectation-maximization algorithm, and Kohonen's self-organizing maps (SOM). The most accurate clustering is yielded by SOM. Afterwards, we turn to real data. The users of a citizen web portal (Infoville XXI, http://www.infoville.es) are clustered. The clustering achieved enables us to study the future success of a collaborative recommender by means of a prediction strategy. New users are recommended according to the cluster in which they have been classified. The suitability of the recommendation is evaluated by checking whether or not the recommended objects correspond to those actually selected by the user. The results show the relevance of the information provided by clustering algorithms in this web portal, and therefore, the relevance of developing a collaborative recommender for this web site.     

Two-level Clustering of Web Sites Using Self-Organizing Maps

Web sites contain an ever increasing amount of information within their pages. As the amount of information increases so does the complexity of the structure of the web site. Consequently it has become difficult for visitors to find the information relevant to their needs. To overcome this problem various clustering methods have been proposed to cluster data in an effort to help visitors find the relevant information. These clustering methods have typically focused either on the content or the context of the web pages. In this paper we are proposing a method based on Kohonen’s self-organizing map (SOM) that utilizes both content and context mining clustering techniques to help visitors identify relevant information quicker. The input of the content mining is the set of web pages of the web site whereas the source of the context mining is the access-logs of the web site. SOM can be used to identify clusters of web sessions with similar context and also clusters of web pages with similar content. It can also provide means of visualizing the outcome of this processing. In this paper we show how this two-level clustering can help visitors identify the relevant information faster. This procedure has been tested to the access-logs and web pages of the Department of Informatics and Telecommunications of the University of Athens.     

个性化电子商务网站系统总体结构模型的研究

随着全球网络技术的普及与推广,网络已成为新的商务平台,电子商务网站就成为了网络商务平台的载体。通过对网站系统的个性化设计,可以为今后更多的电子商务网站提供个性化服务起到借鉴作用。     

ASP.NET下利用动态网页技术生成静态HTML页面的方法

介绍了一种在ASP.NET环境下利用动态网页技术生成静态HTML页面的方法.利用这种技术,网站内容管理人员在添加网页时直接利用后台管理发布程序就把页面存放成HTML静态文件,它有生成页面简单、快速的优点.这种技术对于访问量大的网站尤其适用,可以减轻服务器端运行程序和读取数据库的压力,提高了网站的数据存取效率,生成的静态页面也更利于搜索引擎收录.     

Session-based item recommendation in e-commerce: on short-term intents,reminders, trends and discounts

Many e-commerce sites present additional item recommendations to their visitors while they navigate the site, and ample evidence exists that such recommendations are valuable for both customers and providers. Academic research often focuses on the capability of recommender systems to help users discover items they presumably do not know yet and which match their long-term preference profiles. In reality, however, recommendations can be helpful for customers also for other reasons, for example, when they remind them of items they were recently interested in or when they point site visitors to items that are currently discounted. In this work, we first adopt a systematic statistical approach to analyze what makes recommendations effective in practice and then propose ways of operationalizing these insights into novel recommendation algorithms. Our data analysis is based on log data of a large e-commerce site. It shows that various factors should be considered in parallel when selecting items for recommendation, including their match with the customer’s shopping interests in the previous sessions, the general popularity of the items in the last few days, as well as information about discounts. Based on these analyses we propose a novel algorithm that combines a neighborhood-based scheme with a deep neural network to predict the relevance of items for a given shopping session.