基于知识网格的电子商务推荐系统设计*

提出电子商务网站、知识网格节点和虚拟知识网格服务社区三级结构模型,建立自动实现商品知识获取、聚合和智能推荐的运作机制,设计了基于知识网格的电子商务智能推荐系统结构。     

基于大数据的电商数据信息智能推荐服务及应用研究

随着大数据技术、5G移动网络、物联网和人工智能技术的快速发展,移动互联网和智能手机的普及,数据信息出现了过载现象,如何将数据信息有效地实现智能推荐已成为当前人工智能和大数据领域研究的重点。通过对大数据和电商数据信息智能推荐以及电子商务的概念进行梳理,给出了基于大数据的电商数据信息智能推荐服务及应用系统架构,使大数据在电商领域的应用价值得到了充分体现,对电子商务的进一步发展起到了推动作用。     

基于MyMediaLite平台的推荐方法探究

推荐系统是互联网和电子商务的产物。它是建立在对海量数据训练的基础上的一种智能平台,能够向顾客提供个性化的信息服务和决策。随着电子商务大数据的高速发展,推荐系统正逐渐成为学术界的研究热点之一。针对推荐系统理论性强、内容抽象的特点,本文介绍了以MyMediaLite为平台的个性化推荐实践方案,并详细阐述了其具体的实施过程。通过介绍MyMediaLite的系统结构框架,以及分析基于MyMedia Lite的实验过程,为研究者使用MyMediaLite推荐系统库进行算法研究奠定了基础。     

电子商务推荐系统中推荐策略的自适应性

针对电子商务推荐系统中各种推荐技术的不足，提出推荐策略的自适应方法。用二元组《用户知识，推荐商品》代表推荐环境的根本特征．采用ART神经网络进行自学习，获取推荐环境的不同聚类。每个聚类代表了某种推荐环境，对推荐结果的反馈情况进行统计分析．确定每个聚类的最佳推荐技术。向用户推荐商品时，根据用户所在聚类采用具有最佳推荐质量的推荐技术向用户作出推荐。整个系统的工作过程不需要人工干预，具有自适应性。     

大数据环境下智能推荐系统中协同过滤算法研究

随着互联网的普及以及电子商务的发展,各类网络应用需要大数据的支持,而信息过载是大数据应用最严重的问题之一,需要来自智能推荐系统的支持.分析了大数据环境下智能推荐系统的概念和应用,对协同过滤算法这一智能推荐经典算法进行了阐述,并介绍了其在大数据环境中的应用领域.     

基于多Agent的网络学习智能推荐模型

针对网络学习者面临海量信息选择的困扰,提出了一个基于多Agent的网络学习智能推荐模型.运用界面Agent采实现与学习者的交互,利用基于知识推荐的Agent提供与学习者兴趣相关的推荐,以及基于相似学习者推荐的Agent向特定学习者推荐新的知识,并对模型中推荐的相似度算法进行了阐述.通过多Agent技术的运用,较好的解决了网络学习推荐的智能化,个性化以及灵活性的问题,使网络学习者能在一种交互式的学习环境中得到更人性化的学习推荐服务.     

基于Java的商品智能推荐系统设计

随着互联网信息的增长,Web数据挖掘已经成为广泛应用于电子商务商品智能推荐领城。商家能在网上提供的商品种类和数量非常多,用户既不愿意花太多时间在漫无边际的网上寻找商品,也不可能像在物理环境下那样检查商品的质量。因此,用户很希望电子商务系统具有一种类似采购助手的功能来帮助其选购商品,并能根据用户的兴趣爱好自动地推荐给每个用户可能感兴趣的商品。介绍了基于Java的电子商务商品推荐系统的设计,阐述了系统从需求获取到系统分析和设计再到编程实施的整个过程。     

基于协同过滤的电子商务智能推荐方法研究

为获得更加理想的电子商务推荐结果,提出一种基于协同过滤的电子商务智能推荐方法.该方法收集电子商务用户相关信息,并对信息进行预处理,计算电子商务用户对项目评分,构建电子商务用户评分矩阵,采用余弦算法根据用户评分矩阵计算用户之间的相性度,基于用户相似度进行电子商务智能推荐.为了与其他方法进行比较,开展仿真实验.实验结果表明...     

基于核估计的电子商务协作过滤方法

论文对电子商务推荐技术进行了讨论,针对电子商务推荐技术存在的问题,提出了一种基于核估计的协作过滤方法,首先将推荐项的评分设定为其他人对推荐项评分的平均值,然后利用主成分分析进行降维,最后采用基于核估计的思想对推荐项进行预测评分。实验表明,该方法可以有效解决在用户评分数据极端稀疏情况下传统相似性度量方法存在的问题,大幅提高推荐系统的推荐质量。     

基于协作过滤算法的电子商务个性化推荐系统的研究

针对目前电子商务个性化推荐研究的不足,提出准确全面地获取用户独特兴趣爱好、满足用户差异化需求的推荐服务,同时构建了具体的个性化推荐系统模型,给出了基于协作过滤算法的电子商务个性化推荐的流程、系统设计和系统实现,从而有利于推动电子商务的发展。     

Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning

Recommender systems in e-learning domain play an important role in assisting the learners to find useful and relevant learning materials that meet their learning needs. Personalized intelligent agents and recommender systems have been widely accepted as solutions towards overcoming information retrieval challenges by learners arising from information overload. Use of ontology for knowledge representation in knowledge-based recommender systems for e-learning has become an interesting research area. In knowledge-based recommendation for e-learning resources, ontology is used to represent knowledge about the learner and learning resources. Although a number of review studies have been carried out in the area of recommender systems, there are still gaps and deficiencies in the comprehensive literature review and survey in the specific area of ontology-based recommendation for e-learning. In this paper, we present a review of literature on ontology-based recommenders for e-learning. First, we analyze and classify the journal papers that were published from 2005 to 2014 in the field of ontology-based recommendation for e-learning. Secondly, we categorize the different recommendation techniques used by ontology-based e-learning recommenders. Thirdly, we categorize the knowledge representation technique, ontology type and ontology representation language used by ontology-based recommender systems, as well as types of learning resources recommended by e-learning recommenders. Lastly, we discuss the future trends of this recommendation approach in the context of e-learning. This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations. It was also evident that hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e-learning recommenders.     

Hybrid Recommender Systems: Survey and Experiments

Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.     

Developing recommender systems with the consideration of product profitability for sellers

In electronic commerce web sites, recommender systems are popularly being employed to help customers in selecting suitable products to meet their personal needs. These systems learn about user preferences over time and automatically suggest products that fit the learned model of user preferences. Traditionally, recommendations are provided to customers depending on purchase probability and customers’ preferences, without considering the profitability factor for sellers. This study attempts to integrate the profitability factor into the traditional recommender systems. Based on this consideration, we propose two profitability-based recommender systems called CPPRS (Convenience plus Profitability Perspective Recommender System) and HPRS (Hybrid Perspective Recommender System). Moreover, comparisons between our proposed systems (considering both purchase probability and profitability) and traditional systems (emphasizing an individual’s preference) are made to clarify the advantages and disadvantages of these systems in terms of recommendation accuracy and/or profit from cross-selling. The experimental results show that the proposed HPRS can increase profit from cross-selling without losing recommendation accuracy.     

基于Intelligent Agent的智能电子商务

近年来。网络普及使得电子商务技术得到了长足的发展，但仍存在着很多问题。特别是智能和个性化的电子商务已经成了人们追求的目标。本文试图将Agent技术运用于电子商务系统中，介绍了基于Intelligent Agent的智能电子商务系统的基本概念及目前智能电子商务的发展状况，并就Intelligent Agent的智能电子商务系统的未来发展提出了问题和建议。     

基于深度学习的情境感知推荐系统研究进展

传统的推荐系统存在数据高度稀疏、冷启动及用户偏好建模难等问题,而把情境信息融入推荐系统中能有效缓解此类问题.深度学习技术已经成为人工智能领域研究热点,把深度学习应用在情境感知推荐系统当中,为推荐领域的研究带来新的机遇与挑战.本文从情境感知推荐系统相关概念出发,综合整理国内外研究相关文献,介绍深度学习技术融入情境感知推荐系统相关应用模型,提出了基于深度学习的情境感知推荐系统研究的不足以及对未来的展望.     

基于决策过程的个性化推荐系统设计

电子商务的深度发展要求信息技术必须与商务原理结合。现有的个性化推荐系统大多强调推荐内容的精准性,而忽视了推荐结果的利润性和推荐列表的多样性。从消费者与企业的决策过程出发,将消费者购物决策过程简化为意识与满意,将企业决策分为推荐回应与利润计算,以企业推荐商品的利润最大化为目标,设计了一个三层结构的个性化推荐系统,给出了数据支持层、类别分类层和推荐结果层的逻辑算法。针对该系统的实验结果表明,在推荐精准度、多样性和利润度上,都达到较优效果。     

Generating recommendations for consensus negotiation in group personalization services

There are increasingly many personalization services in ubiquitous computing environments that involve a group of users rather than individuals. Ubiquitous commerce is one example of these environments. Ubiquitous commerce research is highly related to recommender systems that have the ability to provide even the most tentative shoppers with compelling and timely item suggestions. When the recommendations are made for a group of users, new challenges and issues arise to provide compelling item suggestions. One of the challenges a group recommender system must cope with is the potentially conflicting preferences of multiple users when selecting items for recommendation. In this paper, we focus on how individual user models can be aggregated to reach a consensus on recommendations. We describe and evaluate nine different consensus strategies and analyze them to highlight the benefits of group recommendation using live-user preference data. Moreover, we show that the performance is significantly different among strategies.     

New Recommendation Techniques for Multicriteria Rating Systems

Personalization technologies and recommender systems help online consumers avoid information overload by making suggestions regarding which information is most relevant to them. Most online shopping sites and many other applications now use recommender systems. Two new recommendation techniques leverage multicriteria ratings and improve recommendation accuracy as compared with single-rating recommendation approaches. Taking full advantage of multicriteria ratings in personalization applications requires new recommendation techniques. In this article, we propose several new techniques for extending recommendation technologies to incorporate and leverage multicriteria rating information.     

Comparing Recommendation Strategies in a Commercial Context

From an industrial perspective, recommender systems constitute the base technology for providing interactivity and personalization in electronic business-to-consumer marketplaces. Robin Burke distinguishes between five different recommendation techniques: collaborative, content based, utility based, demographic, and knowledge based.     

基于DLL的知识库系统及其实现

基于知识系统的主要内容是其领域知识库,而知识库的关键是其搜索效率和可移植性。文中分析了几种基于知识的系统中常用的知识库运行模式,根据动态连接库(DLL)的特点,提出了一种基于DLL的知识库预编译模式,阐述了基于该模式的知识库系统基本原理,并应用于产品设计领域的智能化知识库系统开发环境中,建立层次化的智能CAD知识库,大大提高知识库搜索效率,取得了令人满意的效果。