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.     

基于矩阵聚类的电子商务网站个性化推荐系统

提出一种基于“矩阵聚类”的电子商务网站个性化推荐系统,通过分析Web server日志文件中的访问页面序列行为数据,构建较高购买者的顾客行为的矩阵模型;并使用一种新型的“矩阵聚类”算法挖掘潜在购买者与较高购买者的相似特征,从而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．该技术特别适合于目前大型的电子商务网站,实验数据表明,该系统是高效并可广泛使用．     

基于DMA的时间序列模式下顾客行为的个性化推荐

动态挖掘算法考虑顾客随时间变化的动态行为轨迹的特性，采取动态追踪，以顾客的动态行为轨迹为依据实现对顾客的个性化推荐。由于行为轨迹中时间段划分跨度对推荐源数据实用价值存在影响，故提出了时间约束定义，同时完成了该算法中自动学习功能的实现。实验结果表明，基于该算法的推荐系统有较高的推荐准确度。     

基于顾客行为的产品推荐方法

在线推荐系统提供购物导航服务,是提高电子商务系统性能的重要方面。论文提出了一种基于在线顾客的点击、阅读等行为的产品推荐方法。包括度量顾客行为,通过一个循环过程获得顾客对各产品属性的关注程度,然后在结合分析的基础上分析顾客偏好,依据偏好选择推荐产品。并用实例说明产品推荐过程。     

用户访问模式聚类分析在网页推荐中的应用

在基于Web使用挖掘的推荐系统中,仅采用关联规则挖掘技术的Web推荐系统在预测用户未来浏览模式时很难取得令人满意的结果。该文将聚类分析方法结合关联规则推荐算法,应用于Web日志文件的挖掘,以改进个性化的推荐方法。实验表明,该算法能够显著地改进推荐测度的精确率指标和综合评价指标。     

Web病毒式营销核心群体挖掘与推荐策略

Web病毒式营销已经成为电子商务领域中的重要营销策略, 核心群体在其中发挥着重要的作用。为了挖掘核心群体并对其进行商品推荐, 在Web客户信任网络(customer trust network, CTN)的基础上考虑了信任度、评价分数以及推荐次数等因素定义了影响度的概念, 提出了以影响度为基础的节点网络影响集的构建方法以及基于网络影响集的核心群体挖掘算法MCGNIS（mining core group based on network-influence set）, 并以挖掘出的核心群体为对象建立了基于网络影响集的推荐模型RCGNIS(recommending model for core group based on network-influence set）, 设计了相应的推荐算法来计算商品对核心群体的可推荐度。实验证明, 以节点网络影响集为基础挖掘出的核心群体在Web客户信任网络中具有较高的网络覆盖率（network-coverage, NC）, 推荐模型RCGNIS具有很好的推荐准确性, 同时又保持了推荐的多样性。     

基于Web数据挖掘的电子商务推荐模型研究

当前电子商务推荐系统的推荐精度和实时性是一对矛盾,针对这个问题,以Web数据挖掘技术在电子商务推荐系统中的应用为重点,设计一个基于Web挖掘的电子商务推荐系统应用框架,整个体系结构分为离线部分和在线部分两部分,并且对在线模块的各部分功能进行详细的介绍.     

个性化推荐推荐系统中基于WEB 的挖掘

Intemet的普及和应用带来了WEB上的信息爆炸,如何基于WEB挖掘技术设计有效的信息推荐算法和推荐系统成为当前的研究热点。开发了一种基于WEB使用的推荐系统WRS（Web Recommendation System）,在该系统中,提出了一种利用图形分割技术聚类用户访问模式的算法,并采用最长公共子序列算法对用户目前的行为进行识别。理论分析和实验结果表明,改进后的模型在推荐质量上有了较大提高。     

基于Web挖掘的电子商务推荐系统应用研究

以Web挖掘在电子商务推荐系统中的应用为重点,设计一个基于Web挖掘的电子商务推荐系统应用框架,并详细的分析了它的组成部分.＃     

一种基于兴趣度知识的推荐系统框架

随着Web Mining技术的应用.基于Web Mining技术的推荐系统得到了迅速发展.本文就此系统作了一些改进,并提出了工作框架RESIK.     

Enhancing Product Recommender Systems on Sparse Binary Data

Commercial recommender systems use various data mining techniques to make appropriate recommendations to users during online, real-time sessions. Published algorithms focus more on the discrete user ratings instead of binary results, which hampers their predictive capabilities when usage data is sparse. The system proposed in this paper, e-VZpro, is an association mining-based recommender tool designed to overcome these problems through a two-phase approach. In the first phase, batches of customer historical data are analyzed through association mining in order to determine the association rules for the second phase. During the second phase, a scoring algorithm is used to rank the recommendations online for the customer. The second phase differs from the traditional approach and an empirical comparison between the methods used in e-VZpro and other collaborative filtering methods including dependency networks, item-based, and association mining is provided in this paper. This comparison evaluates the algorithms used in each of the above methods using two internal customer datasets and a benchmark dataset. The results of this comparison clearly show that e-VZpro performs well compared to dependency networks and association mining. In general, item-based algorithms with cosine similarity measures have the best performance.     

Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations

Collaborative filtering (CF)-based recommender systems represent a promising solution for the rapidly growing mobile music market. However, in the mobile Web environment, a traditional CF system that uses explicit ratings to collect user preferences has a limitation: mobile customers find it difficult to rate their tastes directly because of poor interfaces and high telecommunication costs. Implicit ratings are more desirable for the mobile Web, but commonly used cardinal (interval, ratio) scales for representing preferences are also unsatisfactory because they may increase estimation errors. In this paper, we propose a CF-based recommendation methodology based on both implicit ratings and less ambitious ordinal scales. A mobile Web usage mining (mWUM) technique is suggested as an implicit rating approach, and a specific consensus model typically used in multi-criteria decision-making (MCDM) is employed to generate an ordinal scale-based customer profile. An experiment with the participation of real mobile Web customers shows that the proposed methodology provides better performance than existing CF algorithms in the mobile Web environment.     

E-Commerce Technology: Back to a Prominent Future

E-commerce is a big business with a growing market size and has been a major driving force in the IT industry for the past decade. Companies now need to provide online shopping or marketing Web presence to allow for direct customer connections. In this article, the author reviews some primary e-commerce technologies, including auctions, negotiation, recommender systems, automated shopping, and trading. This paper also looks at how Web 2.0 provides new e-commerce opportunities.     

Personalization of Supermarket Product Recommendations

We describe a personalized recommender system designed to suggest new products to supermarket shoppers. The recommender functions in a pervasive computing environment, namely, a remote shopping system in which supermarket customers use Personal Digital Assistants (PDAs) to compose and transmit their orders to the store, which assembles them for subsequent pickup. The recommender is meant to provide an alternative source of new ideas for customers who now visit the store less frequently. Recommendations are generated by matching products to customers based on the expected appeal of the product and the previous spending of the customer. Associations mining in the product domain is used to determine relationships among product classes for use in characterizing the appeal of individual products. Clustering in the customer domain is used to identify groups of shoppers with similar spending histories. Cluster-specific lists of popular products are then used as input to the matching process. The recommender is currently being used in a pilot program with several hundred customers. Analysis of results to date have shown a 1.8% boost in program revenue as a result of purchases made directly from the list of recommended products. A substantial fraction of the accepted recommendations are from product classes new to the customer, indicating a degree of willingness to expand beyond present purchase patterns in response to reasonable suggestions.     

Mining affective needs of automotive industry customers for building a mass-customization recommender system

Mass customization systems aim to receive customer preferences in order to facilitate personalization of products and services. Current online configuration systems are unable to efficiently identify real customer affective needs because they offer an excess variety of products that usually confuse customers. On the other hand, mining affective customer needs may result in recommender systems, which can enhance existing configuration systems by recommending initial configurations according to customer affective needs. This paper introduces a mass customization recommender system that exploits data mining techniques on automotive industry customer data aiming at revealing associations between user affective needs and the design parameters of automotive products. One key novelty of the presented approach is that it deploys the Citarasa engineering, a methodology that focuses on the provision of the appropriate characterizations on customer data in order to associate them with customer affective needs. Based on the application of classification techniques we build a recommendation engine, which is evaluated in terms of user satisfaction, tool’s effectiveness, usefulness and reliability among other parameters.     

基于电子商务的客户访问模式算法的分析研究

通过对电子商务中服务器上的日志文件等Web数据进行客户访问信息的分析,重点研究了客户分析系统的数据采集、数据处理以及跟踪客户在Web上的浏览行为并进行模式分析,并构建了用户访问模式的挖掘模型及算法的分析与实现。     

社会化推荐系统的研究进展综述

对社会网络环境下构建个性化推荐系统的现有技术进行综述。介绍社会网络的基本概念,简述推荐系统的应用领域和目前面临的挑战,重点介绍社会化推荐的相关技术的研究现状,包括用户生成内容、社会化标签推荐、博客挖掘和基于信任的推荐,分析社会化推荐面临的主要问题。利用Web 2．0环境下的用户生成内容,为解决用户配置和冷启动问题提供一个研究方向。     

Handling sequential pattern decay: Developing a two-stage collaborative recommender system

This study proposes a sequential pattern based collaborative recommender system that predicts the customer’s time-variant purchase behavior in an e-commerce environment where the customer’s purchase patterns may change gradually. A new two-stage recommendation process is developed to predict customer purchase behavior for the product categories, as well as for product items. The time window weight is introduced to produce sequential patterns closer to the current time period that possess a larger impact on the prediction than patterns relatively far from the current time period. This study is the first to propose time-decaying sequential patterns within a collaborative recommender system. The experimental results show that the proposed system outperforms the traditional collaborative system using a public food mart dataset and a synthetic dataset.