利用ASP．NET2．0实现动态的商品推荐

本文主要针对中小型电子商务系统，基于访问者的偏爱或从拥有类似偏好的其他访问者那里收集到数据。向访问者提供商品推荐，实现一个简单而有效的商品推荐系统。     

基于多级客户模型的个性化推荐机制

个性化是未来Web智能系统的一大特征．为了实现商品的个性化推荐，提出了一种新的基于多级客户模型的推荐系统机制，它由数据准备、模型学习、推荐集的生成和智能过滤四个子过程构成．该机制借助于多级客户模型从客户的购物需求、偏爱特征和消费能力三方面捕获客户的实际需求，从而实现了一种深层次的个性化推荐，改善了推荐效果．     

基于多维关系和用户聚类的智慧图书馆个性化图书推荐研究

如何为用户提供感兴趣的个性化推荐图书商品向来都是智慧图书馆最核心的难题之一。因此，利用优质的推荐算法构建推荐系统就变得尤为重要。基于多维关系和用户聚类两个方面构建推荐算法，在充分考虑用户之间相关关系和图书商品之间相关关系的基础上，不断更新“用户-图书商品”二维矩阵，使其数值更加合理和真实。随后使用k均值聚类方法聚拢高相关性用户。最后在类内选取目标用户实现个性化图书商品推荐。实验结果表明，该推荐算法能取得较高的评价指标F1值，即算法更加优质和有效。     

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.     

层次凝聚算法在商品个性化推荐中的应用

商品的个性化推荐是电子商务个性化服务中非常重要的一个方面，而聚类协作过滤则是推荐系统中采用最为广泛的技术。在基于聚类协作过滤的商品个性化推荐中的聚类算法通常采用划分聚类，文章根据电子商务网站的特点，提出了用改进的Rock层次凝聚算法Improved-Rock实现基于购买商品类别相似性的用户聚类。模拟实验结果表明该算法的应用是有实际价值的。     

基于用户行为的个性化推荐系统的设计与应用①

目前电子商务网站提供的推荐服务很难满足用户的个性化需求,协同过滤算法作为应用最成功的推荐算法,依然存在数据稀疏性、用户评分真实性等问题,制约着推荐系统的质量。设计和实现了一个基于用户行为的个性化商品推荐系统,主要采用前融合组合推荐策略,避免了单纯使用协同过滤算法的弱点。阐述了基于用户行为的个性化推荐系统的设计思想和实现过程,最终通过实验验证了本推荐系统具有良好的推荐效果。     

基于协同过滤的水果推荐系统设计与实现

随着大数据技术的飞速发展,从大量的信息中如何让用户发现和挖掘出有价值的信息,一直是人们研究的热点问题。推荐系统的发展起到了关键作用,主要是发现用户和商品之间的信息,一方面为用户找到有价值的信息,另一方面为用户推荐感兴趣的商品,从而实现了用户和信息生成者的共赢。基于协同过滤的水果推荐系统通过分析用户的历史行为了解用户的喜好,在为用户提供其感兴趣的信息的同时,也能够实现个性化的推荐。     

B2C网上购物推荐系统的设计与实现

根据B2C网上购物的实际背景和目前推荐系统存在的不足,设计和实现了一个运用多种技术相结合的个性化网页推荐系统.这种推荐模型增强了推荐系统的实时性,提高了推荐服务的质量,从而提高了电子商务网站的交叉销售能力和网站商品的销售量.该系统所用的方法、处理过程和推荐形式,对于其他电子商务网站也都有一定的借鉴意义.     

基于用户行为数据的电网企业营销推荐系统设计

由于目前方法未能分析和挖掘电网用户行为,使用户的商品属性偏好与预计营销偏好存在差异,导致电网企业营销推荐结果不理想,为此提出基于用户行为数据的电网企业营销推荐系统。通过系统硬件和软件相互协作设计,从用户历史行为出发,优先分析处理用户的历史交互行为,对用户的行为喜好进行分类,挖掘用户的商品属性偏好,实现用户近期需求预测以及意向商品推荐。实验结果证明,所设计系统能够有效提升推荐速率和用户满意度,获取效果较好的推荐结果。     

一种基于整体多样性增强的推荐算法

目前大多数推荐算法都是以提高用户对未知商品的预测评分值为主要目标,然而预测准确率并不是增加用户满意度的唯一标准,推荐列表的多样性也是衡量推荐质量的一个重要指标。提出了一种新的推荐方法,旨在提高系统的整体多样性和长尾商品的推荐率。算法综合考虑了商品预测值、商品流行度、商品的偏爱度等多个标准。实验表明,与其他方法相比,本方法在维持较高推荐准确率的同时,能够推荐更多的长尾商品,提高了系统的整体多样性。     

Informed Recommender: Basing Recommendations on Consumer Product Reviews

Recommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques (see the sidebar for more details). We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations. Informed recommender uses prioritized consumer product reviews to make recommendations. Using text-mining techniques, it maps each piece of each review comment automatically into an ontology.     

Recommender System to Analyze Student’s Academic Performance

A sufficient amount of studies worldwide prove an interrelation linking student learning productivity and interest in learning to physiological parameters. An interest in learning affects learning productivity, while physiological parameters demonstrate such changes. Since the research by the authors of the present article confirmed these interdependencies, a Recommender System to Analyze Student’s Academic Performance (Recommender System hereafter) has been developed. The Recommender System determines the level of learning productivity integrally by employing three main techniques (physiological, psychological and behavioral). This Recommender System, developed by these authors, uses motivational, educational persistence and social learning theories and the database of best global practices based on above theories to come up with recommendations for students on how to improve their learning efficiency. The Recommender System can pick learning materials taking into account a student’s learning productivity and the degree to which learning is interesting. Worldwide research includes various scientists who conducted in-depth studies on the different and very important areas of physiological measurements and intelligent systems. We did not manage to find any physiological measurements or any intelligent or integrated system that would take physiological parameters of students, analyze their learning efficiency and, in turn, provide recommendations.     

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.     

Privacy risks in recommender systems

Recommender system users who rate items across disjoint domains face a privacy risk analogous to the one that occurs with statistical database queries     

Product recommendation for e-commerce business by applying principal component analysis (PCA) and K-means clustering: benefit for the society

Innovations in Systems and Software Engineering - Recommender system is a computer-based intelligent technique which facilitates the customers to fulfill their purchase requirements. In addition to...     

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．     

A tourism destination recommender system using users’ sentiment and temporal dynamics

With the development and popularity of social networks, an increasing number of consumers prefer to order tourism products online, and like to share their experiences on social networks. Searching for tourism destinations online is a difficult task on account of its more restrictive factors. Recommender system can help these users to dispose information overload. However, such a system is affected by the issue of low recommendation accuracy and the cold-start problem. In this paper, we propose a tourism destination recommender system that employs opinion-mining technology to refine user sentiment, and make use of temporal dynamics to represent user preference and destination popularity drifting over time. These elements are then fused with the SVD+ + method by combining user sentiment and temporal influence. Compared with several well-known recommendation approaches, our method achieves improved recommendation accuracy and quality. A series of experimental evaluations, using a publicly available dataset, demonstrates that the proposed recommender system outperforms the existing recommender systems.     

Building a fuzzy logic-based McCulloch-Pitts Neuron recommendation model to uplift accuracy

The Journal of Supercomputing - Recommender system is one of the most popular technique used for information filtering. It helps in discovering hidden knowledge patterns from a large set of...     

Novel Positive Multi-Layer Graph Based Method for Collaborative Filtering Recommender Systems

Recommender systems play an increasingly important role in a wide variety of applications to help users find favorite products. Collaborative filtering has remarkable success in terms of accuracy and becomes one of the most popular recommendation methods. However, these methods have shown unpretentious performance in terms of novelty, diversity, and coverage. We propose a novel graph-based collaborative filtering method, namely Positive Multi-Layer Graph-Based Recommender System (PMLG-RS). PMLG-RS involves a positive multi-layer graph and a path search algorithm to generate recommendations. The positive multi-layer graph consists of two connected layers: the user and item layers. PMLG-RS requires developing a new path search method that finds the shortest path with the highest cost from a source node to every other node. A set of experiments are conducted to compare the PMLG-RS with well-known recommendation methods based on three benchmark datasets, MovieLens-100K, MovieLens-Last, and Film Trust. The results demonstrate the superiority of PMLG-RS and its high capability in making relevant, novel, and diverse recommendations for users.

     

Securing Recommender Systems Against Shilling Attacks Using Social-Based Clustering

Recommender systems (RS) have been found supportive and practical in e-commerce and been established as useful aiding services. Despite their great adoption in the user communities, RS are still vulnerable to unscrupulous producers who try to promote their products by shilling the systems. With the advent of social networks new sources of information have been made available which can potentially render RS more resistant to attacks. In this paper we explore the information provided in the form of social links with clustering for diminishing the impact of attacks. We propose two algorithms, CluTr and WCluTr, to combine clustering with "trust" among users. We demonstrate that CluTr and WCluTr enhance the robustness of RS by experimentally evaluating them on data from a public consumer recommender system Epinions.com.