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一种集成客户终身价值与协同过滤的推荐方法 总被引：1，自引：0，他引：1
提出一种加权RFM与协同过滤相结合的集成推荐方法,对由"Web数据挖掘"隐式收集的客户评价数据进行协同过滤处理,应用加权RFM对相似用户聚类结果加以改进,从而更有效地发现推荐规则,提高推荐质量。同时应用产品分类树(PT)对产品进行预处理,以减少计算空间的复杂度。实验评价结果表明该方法无论在推荐精度还是推荐相关性上都更为有效。 相似文献
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Information filtering is an area getting more important as we have long been flooded with too much information, where product brokering in e-commerce is a typical example. Systems which can provide personalized product recommendations to their users (often called recommender systems) have gained a lot of interest in recent years. Collaborative filtering is one of the commonly used approaches which normally requires a definition of user similarity measure. In the literature, researchers have proposed different choices for the similarity measure using different approaches, and yet there is no guarantee for optimality. In this paper, we propose the use of machine learning techniques to learn the optimal user similarity measure as well as user rating styles for enhancing recommendation acurracy. Based on a criterion function measuring the overall prediction error, several ratings transformation functions for modeling rating styles together with their learning algorithms are derived. With the help of the formulation and the optimization framework, subjective components in user ratings are removed so that the transformed ratings can then be compared. We have evaluated our proposed methods using the EachMovie dataset and succeeded in obtaining significant improvement in recommendation accuracy when compared with the standard correlation-based algorithm. 相似文献
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[目的/意义]推荐系统已经成为电子商务网站的重要组成部分之一,为用户提供多种形式的信息推荐服务。国内以淘宝、京东和亚马逊为代表的电子商务网站的推荐系统采用不同的技术架构和多种热点推荐技术,并且越来越重视信息服务的质量。对推荐系统服务质量进行比较研究,能够进一步推动电子商务推荐系统的发展。[方法/过程]首先,从准确性、时效性、新颖性三个技术指标对比以上推荐系统的技术架构对于推荐服务质量的影响;其次,以用户体验作为信息服务质量评价的基础,对182名受访者进行热点技术的认可度调查,研究热点技术对推荐服务质量的影响;最后,对功能模块的用户体验情况进行调查和比较分析。[结果/结论]在这些研究、调查和分析的基础上,给出电子商务推荐系统使用的技术架构和热点技术,以及改进功能模块设计的对策,以进一步提升推荐系统的信息服务质量。 相似文献
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Grace Burchard 《图书情报工作》2007,51(12):33-33
Recommender systems can be a powerful tool for digital libraries if they implement the systems right to information seekers. While patrons have shown an increased interest in recommender systems, digital libraries ought to offer personalized services and assist users both novice and experienced with more sophisticate information retrieval technologies. By guiding users to timely, accurate, and suitable resources within the library the recommender system could take the place of the reference librarian of the bricks and mortar library to enhance the digital libraries services. 相似文献
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《Library & information science research》2022,44(4):101191
The use of data mining modern technology in library management systems and information centers is of great importance. With the increasing availability of a large quantity of information, traditional tools and practices without wasting time and cost cannot respond to users accurately and quickly. The present study aims to analyze book circulation transactions and discover the user's book loan patterns to develop a recommender system. The data included 109,639 transactions and information from 8636 user records. Microsoft SQL Server and Matlab software were applied to analyze the data. Item-based collaborative filtering algorithms and decision tree methods were also applied. The results led to the extraction of rules for suggesting books to users. Analysis of the circulation data could be applied to address many issues like evaluation, collection acquisition policies, allocating funding for materials, and suggesting approaches to deselecting and allocating physical space for materials. 相似文献
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[目的/意义]科研社交网络与大众社交网络一样存在信息过载问题,利用推荐系统向科研人员推送个性化信息是解决该问题的重要手段。通过与国外主流科研社交网络相比较,找出我国科研社交网络的推荐系统存在的问题,进而寻求解决之道。[方法/过程]从推荐项目、推荐策略、冷启动方案、用户偏好学习4个方面,对科研之友、学者网、ResearchGate、Academia这4个科研社交网络的推荐系统进行对比。[结果/结论]我国科研社交网络的推荐系统在上述4个方面都与国外同行存在明显的差距,存在推荐项目较少、推荐策略单一、冷启动效果差、用户偏好学习能力弱等问题。针对这些问题,提出改进建议。 相似文献
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提出一个移动互联网环境下用于个性化信息服务的基于情境历史的移动用户偏好挖掘方法,并构建移动旅游信息推荐原型系统CAMTRS。实验结果显示:该方法能较好地获取移动互联网环境下用户的需求偏好,有助于改进个性化推荐系统的预测效果。 相似文献
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Privacy-preserving collaborative filtering algorithms are successful approaches. However, they are susceptible to shilling attacks. Recent research has increasingly focused on collaborative filtering to protect against both privacy and shilling attacks. Malicious users may add fake profiles to manipulate the output of privacy-preserving collaborative filtering systems, which reduces the accuracy of these systems. Thus, it is imperative to detect fake profiles for overall success. Many methods have been developed for detecting attack profiles to keep them outside of the system. However, these techniques have all been established for non-private collaborative filtering schemes. The detection of shilling attacks in privacy-preserving recommendation systems has not been deeply examined. In this study, we examine the detection of shilling attacks in privacy-preserving collaborative filtering systems. We utilize four attack-detection methods to filter out fake profiles produced by six well-known shilling attacks on perturbed data. We evaluate these detection methods with respect to their ability to identify bogus profiles. Real data-based experiments are performed. Empirical outcomes demonstrate that some of the detection methods are very successful at filtering out fake profiles in privacy-preserving collaborating filtering schemes. 相似文献
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文献推荐系统：提高信息检索效率之途 总被引：2，自引：0，他引：2
Traditional Information Retrieval (IR) systems have limitations in improving search performance in today’s information environment. The high recall and poor precision of traditional IR systems are only as good as with the accuracy of search query, which is, however, usually difficult for the user to construct. It is also time-consuming for the user to evaluate each search result. The recommendation techniques having been developed since the early 1990s help solve the problems that traditional IR systems have. This paper explains the basic process and major elements of document recommender systems, especially the two recommendation techniques of content-based filtering and collaborative filtering. Also discussed are the evaluation issue and the problems that current document recommender systems are facing, which need to be taken into account in future system designs. Traditional Information Retrieval (IR) systems have limitations in improving search performance in today’s information environment. The high recall and poor precision of traditional IR systems are only as good as with the accuracy of search query, which is, however, usually difficult for the user to construct. It is also time-consuming for the user to evaluate each search result. The recommendation techniques having been developed since the early 1990s help solve the problems that traditional IR systems have. This paper explains the basic process and major elements of document recommender systems, especially the two recommendation techniques of content-based filtering and collaborative filtering. Also discussed are the evaluation issue and the problems that current document recommender systems are facing, which need to be taken into account in future system designs. 相似文献
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R P Lutz 《Bulletin of the Medical Library Association》1971,59(2):254-261
Information centers are being established for many disciplines. For the medical profession, users can benefit directly from these centers by having information searched by medical library professionals and readily available. If the users of an information system are to share in the operating expenses, some equitable system of charges must be established. The numerous systems of establishing user charges are listed and discussed, with the advantages or disadvantages of each system explained. After the systems have been reviewed, alternative methods of establishing prices are presented along with a typical example of what these prices might be, ranging from $7.50 to $2.50 per request. The implementation of the cost system is outlined and certain philosophical questions are posed. 相似文献
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提出基于不完全模糊语言的高校数字图书馆信息资源推荐系统,该系统中,用户兴趣模型的建立不要求用户直接提供偏好信息,而是允许用户通过不完全模糊语言偏好关系来表达个人偏好,这样既为用户节省时间和精力,又能获取更加准确的用户偏好,从而大大提高推荐精度。系统同时还引入"用户协作偏好",有助于用户开展多学科研究或参与合作研究项目。 相似文献
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电子商务隐式浏览输入中的用户聚类分析 总被引：2，自引：0，他引：2
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Borrowing From e-commerce; Are Recommender Systems Good for Libraries? For past 10 years recommender systems have attempted to match customers to materials in the e-commerce world. Utilizing specifically designed recommender systems to meet the needs of patrons, collaborative filtering, and content-based recommender systems are the two basic types with several hybrids created from these two have been significantly enhancing digital library services. Security issues come into play in a special way with libraries, and plagiarism can also affect recommender quality. Open Source tools may be the answer for libraries and their customers, enabling a better utilization of all that a library has to offer. 相似文献
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在描述移动电子商务推荐系统的基本特征基础上,分析了显式评分输入和隐式浏览输入的差异,
认为移动互联网环境下隐式浏览输入是推荐输入的主流。进而通过用户兴趣提取、用户兴趣计算以及浏
览时间确定等环节,得到移动环境下用户对产品的兴趣度。该方法的提出一方面充实了移动推荐系统的
理论研究成果,另一方面也对推荐系统中隐式浏览输入的研究有一定的推动作用。 相似文献
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