共查询到19条相似文献，搜索用时 140 毫秒
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本文结合向量空间技术和概念检索技术提出了基于概念的数字图书馆信息过滤系统，该系统能够从词汇所表达的概念意义层次上采处理文档与用户的信息需求．系统根据用户提供的初始信息和反馈信息建立并更新用户模板，并在此基础上，主动从大量的动态信息流中挑选出满足用户需求的信息推送给用户。 相似文献
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本文介绍了实现个性化网络服务的一种方法，即构建面向用户兴趣的网页信息过滤系统，同时分析了这种过滤系统中关干用户兴趣的表示方法、网页信息过滤系统的一般模型以及信息过滤系统的主要评估方法，这些研究可以为下一步系统的实现提供理论基础。 相似文献
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从信息过滤系统模型功能及其实现出发,探讨了Google中PageRank技术环境下的用户偏好的分析描述与表达.在传统的过滤算法的基础上进行了改进后的过滤算法的模型构建和原形研究,结合实践进行了实验结果分析. 相似文献
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面向情境兴趣的文本信息过滤系统 总被引：3，自引：0，他引：3
随着对信息过滤技术研究的深入和展开，信息过滤系统的推荐质量不断得到提高。但长期以来文本信息过滤系统都是用来推荐符合用户长期兴趣的信息，而忽略了用户对短期突发信息的需求。针对这种情况，本文提出了面向个人情境兴趣的文本信息过滤系统。模拟实验显示，这种过滤系统可以更好的适用用户信息需求快速变化的情况。 相似文献
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采用信息协同过滤技术构建一个面向公众的电子政务信息推荐服务系统框架。通过信息用户评价矩阵寻找相似度较高的邻居集,能够较好地把握用户的需求偏好,主动提供适合用户的信息组合。但需要解决稀疏性、冷启动、扩展瓶颈问题,以及处理好用户参与、个人隐私和系统优化问题。 相似文献
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由于网络用户数据呈现稀疏状导致对用户信任值评价控制不准确,难以实现有效推荐。传统方法中使用语义Web结合协同过滤方法进行用户信任网络控制,在协同过滤中由于冷启动和不确定时延导致相似用户发现较为困难。提出一种基于Lyapunov稳定性理论的用户信任度评价渐进控制算法,完成用户信任网络控制器设计,并进行用户推荐模型构建,实现对用户信任度的准确评价。仿真实验表明,使用该算法和模型进行网络交易用户信任评价,能解决评价系统中存在的不确定误差、干扰和时滞等问题,用户推荐模型鲁棒性好。 相似文献
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协同过滤技术是个性化推荐系统中最经典的代表,但传统的协同过滤技术也面临着冷启动、数据稀疏性等弊端,加上协同过滤技术很少考虑用户兴趣随时间变化和用户特征等因素,导致推荐质量不尽如人意。在传统协同过滤的基础上,结合用户兴趣变化和用户特征两方面,提出一种改进算法的协同过滤技术,与传统技术相比推荐质量显著提高。 相似文献
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基于单客户端的垃圾邮件过滤系统面对技术越来越高明的垃圾邮件发布者已经突现出它的弱点,多Agent技术为垃圾邮件过滤系统的设计提供了新的思路。旨在将Multi-agent技术和协同过滤的思想引入到垃圾邮件过滤系统中,设计一个多层次垃圾邮件过滤系统。该系统针对垃圾邮件一般群发给多人的特点,提取用户的操作和阅读速度进行反馈,利用他人的反馈结果进行协同过滤。 相似文献
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Information filtering (IF) systems usually filter data items by correlating a set of terms representing the user’s interest (a user profile) with similar sets of terms representing the data items. Many techniques can be employed for constructing user profiles automatically, but they usually yield large sets of term. Various dimensionality-reduction techniques can be applied in order to reduce the number of terms in a user profile. We describe a new terms selection technique including a dimensionality-reduction mechanism which is based on the analysis of a trained artificial neural network (ANN) model. Its novel feature is the identification of an optimal set of terms that can classify correctly data items that are relevant to a user. The proposed technique was compared with the classical Rocchio algorithm. We found that when using all the distinct terms in the training set to train an ANN, the Rocchio algorithm outperforms the ANN based filtering system, but after applying the new dimensionality-reduction technique, leaving only an optimal set of terms, the improved ANN technique outperformed both the original ANN and the Rocchio algorithm. 相似文献
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Vreixo Formoso Diego Fernández Fidel Cacheda Victor Carneiro 《Information processing & management》2013
Collaborative Filtering techniques have become very popular in the last years as an effective method to provide personalized recommendations. They generally obtain much better accuracy than other techniques such as content-based filtering, because they are based on the opinions of users with tastes or interests similar to the user they are recommending to. However, this is precisely the reason of one of its main limitations: the cold-start problem. That is, how to recommend new items, not yet rated, or how to offer good recommendations to users they have not information about. For example, because they have recently joined the system. In fact, the new user problem is particularly serious, because an unsatisfied user may stop using the system before it could even collect enough information to generate good recommendations. In this article we tackle this problem with a novel approach called “profile expansion”, based on the query expansion techniques used in Information Retrieval. In particular, we propose and evaluate three kinds of techniques: item-global, item-local and user-local. The experiments we have performed show that both item-global and user-local offer outstanding improvements in precision, up to 100%. Moreover, the improvements are statistically significant and consistent among different movie recommendation datasets and several training conditions. 相似文献
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基于信息过滤的Web信息查询优化 总被引：2，自引：0，他引：2
从信息过滤的角度分析信息查询的个性化发展,通过用户需求与信息内容的相似性匹配过滤与用户需求无关的信息,从而实现网络环境下用户信息查询结果的优化.据此建立基于信息过滤的用户模型框架,探讨基于信息过滤的信息查询系统优化实现的过程和方法. 相似文献
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Jamal Abdul Nasir Iraklis Varlamis Samreen Ishfaq 《Information processing & management》2019,56(5):1605-1617
Searching for relevant material that satisfies the information need of a user, within a large document collection is a critical activity for web search engines. Query Expansion techniques are widely used by search engines for the disambiguation of user’s information need and for improving the information retrieval (IR) performance. Knowledge-based, corpus-based and relevance feedback, are the main QE techniques, that employ different approaches for expanding the user query with synonyms of the search terms (word synonymy) in order to bring more relevant documents and for filtering documents that contain search terms but with a different meaning (also known as word polysemy problem) than the user intended. This work, surveys existing query expansion techniques, highlights their strengths and limitations and introduces a new method that combines the power of knowledge-based or corpus-based techniques with that of relevance feedback. Experimental evaluation on three information retrieval benchmark datasets shows that the application of knowledge or corpus-based query expansion techniques on the results of the relevance feedback step improves the information retrieval performance, with knowledge-based techniques providing significantly better results than their simple relevance feedback alternatives in all sets. 相似文献
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网络信息过滤一直是个有争议的问题,但信息污染的严重性,迫使各国分别出台控制有害信息的法律法规,我国也在相关的法律法规中明确规定对非法与有害信息的控制,但由于缺乏有力的监管,网络上有害信息众多,文章就此提出了从信源、信道、信宿构建监督执行网络信息过滤的体系。 相似文献
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基于内容的智能网络多媒体信息过滤检索 总被引：7，自引：2，他引：7
The paper discusses the construction of a content-based intelligent system that performs multimedia information filtering and retrieving on the Internet. The system disassembles the multimedia information into different media objects and describes them with vectors for content-based retrieval. In the user study module, the system uses the BP neural network to clarify the user interests for intelligent filtering and retrieving. 相似文献
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《Information processing & management》1999,35(5):633-654
New methods and new systems are needed to filter or to selectively distribute the increasing volume of electronic information being produced nowadays. An effective information filtering system is one that provides the exact information that fulfills user's interests with the minimum effort by the user to describe it. Such a system will have to be adaptive to the user changing interest. In this paper we describe and evaluate a learning model for information filtering which is an adaptation of the generalized probabilistic model of Information Retrieval. The model is based on the concept of `uncertainty sampling', a technique that allows for relevance feedback both on relevant and nonrelevant documents. The proposed learning model is the core of a prototype information filtering system called ProFile. 相似文献
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[目的/意义]为提高知识付费平台用户感知服务质量,文章构建了融合用户画像与协同过滤的个性化推荐模型。[方法/过程]首先根据用户特性构建画像标签体系,利用TF-IDF、熵值法、k-means等方法确定用户特征标签;其次分别基于用户画像与改进后的协同过滤算法计算用户相似度,通过调和权重得到用户综合相似度;最后利用Top-N进行个性化推荐。[结果/讨论]通过知乎live付费用户信息进行验证,发现本文算法在推荐结果的准确率以及召回率上,相比其单一方法均有较大提升,且满意度高于知乎live平台。 相似文献
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大数据环境下,推荐系统项目评分的稀疏性问题愈加突出,单兴趣表示方法也难以对用户多种情境兴趣进行准确描述,导致推荐结果精度大大降低。鉴于此,提出一种多情境兴趣表示方法,在此基础上构建面向图书馆大数据知识服务的多情境兴趣推荐模型,通过对用户多情境兴趣的层次划分,利用蚁群层次挖掘的优势来发现目标用户的若干最近邻类簇,然后根据类簇内相似用户对目标项目的评分对未评分项目进行预测,最后结合MapReduce化的大数据并行处理方法来进行协同过滤推荐。实验结果表明,多情境兴趣的建模方法改善了单兴趣建模存在的歧义推荐问题,有效缓解了大数据环境下项目评分的数据稀疏问题,MapReduce化的蚁群层次聚类方法也大大改善了推荐系统的运行效率。 相似文献