共查询到20条相似文献，搜索用时 390 毫秒
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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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随着互联网中信息量以极快的速度增长,人们虽然已经可以运用大量的信息收集工具获得网络上的信息,却很难从海量的信息当中获得符合自己个性化需求的内容。因此,为了能高准确率、高回收率地解决以上问题,本文实现了一种基于遗传算法的文本过滤技术。根据用户的需求,通过对信息内容进行分析,在动态的信息流中搜索用户感兴趣的信息,屏蔽无关信息,达到对信息的有效过滤。 相似文献
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本文以对互联网上的突发事件报道为研究领域,探讨了信息过滤系统中用户兴趣的变化,并对变化进行了归类和分析,提出了对不同用户兴趣变化,类层次结构模型如何有效的快速适应这种变化的策略。 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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基于协同过滤（CF）的个性化推荐技术,提出一种移动设备个性化软件推荐系统.该系统根据协同过滤的理论,首先通过软件类别兴趣相似度的计算,筛选出软件类别相似的用户候选集,过滤所有移动用户,减小产生的用户候选推荐集;然后对用户候选推荐集进行最近邻居的相似性计算以找出目标用户的邻居集合,并且对邻居集合中的邻居评分进行实时更新;最后根据兴趣相似度最大的K个邻居形成目标用户的Top-N推荐集.在第三方手机软件管理平台上通过监测推荐软件的下载或浏览量,验证系统的有效性和准确性. 相似文献
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论述了网络信息过滤的原理,从用户的信息需求与表示、文本的表示技术等方面探讨了网络信息过滤的方法和技术以及存在的问题,提出Vague集之间的相似度量在网络信息过滤中的应用。 相似文献
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针对创新社区日益增长的海量信息阻碍了用户对知识进行有效获取和创造的现状,将模糊形式概念分析（FFCA）理论应用于创新社区领先用户的个性化知识推荐研究。首先识别出创新社区领先用户并对其发帖内容进行文本挖掘得到用户——知识模糊形式背景,然后构建带有相似度的模糊概念格对用户偏好进行建模,最后基于模糊概念格和协同过滤的推荐算法为领先用户提供个性化知识推荐有序列表。以手机用户创新社区为例,验证了基于FFCA的领先用户个性化知识推荐方法的可行性,有助于满足用户个性化知识需求,促进用户更好地参与社区知识创新。 相似文献
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在移动环境下让用户对博客进行直接评分有很多弊端.因此,如何获取用户对博客的评分信息已成为一个亟待解决的问题.基于隐性评分技术,通过分析用户阅读博客时的阅读速度和阅读文章的比例,计算出用户对博客的偏好信息,进而将传统的基于项目的协同过滤技术应用到博客推荐中,提出了移动环境下基于隐性评分的协同过滤博客推荐技术.最后,通过实验证明该技术可以在移动环境下有效地为用户推荐符合其兴趣的博客. 相似文献
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Modeling user profiles is a necessary step for most information filtering systems – such as recommender systems – to provide personalized recommendations. However, most of them work with users or items as vectors, by applying different types of mathematical operations between them and neglecting sequential or content-based information. Hence, in this paper we study how to propose an adaptive mechanism to obtain user sequences using different sources of information, allowing the generation of hybrid recommendations as a seamless, transparent technique from the system viewpoint. As a proof of concept, we develop the Longest Common Subsequence (LCS) algorithm as a similarity metric to compare the user sequences, where, in the process of adapting this algorithm to recommendation, we include different parameters to control the efficiency by reducing the information used in the algorithm (preference filter), to decide when a neighbor is considered useful enough to be included in the process (confidence filter), to identify whether two interactions are equivalent (δ-matching threshold), and to normalize the length of the LCS in a bounded interval (normalization functions). These parameters can be extended to work with any type of sequential algorithm.We evaluate our approach with several state-of-the-art recommendation algorithms using different evaluation metrics measuring the accuracy, diversity, and novelty of the recommendations, and analyze the impact of the proposed parameters. We have found that our approach offers a competitive performance, outperforming content, collaborative, and hybrid baselines, and producing positive results when either content- or rating-based information is exploited. 相似文献
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A recommender system has an obvious appeal in an environment where the amount of on-line information vastly outstrips any individual’s capability to survey. Music recommendation is considered a popular application area. In order to make personalized recommendations, many collaborative music recommender systems (CMRS) focus on capturing precise similarities among users or items based on user historical ratings. Despite the valuable information from audio features of music itself, however, few studies have investigated how to utilize information extracted directly from music for personalized recommendation in CMRS. In this paper, we describe a CMRS based on our proposed item-based probabilistic model, where items are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. In addition, this model has been extended for improved recommendation performance by utilizing audio features that help alleviate three well-known problems associated with data sparseness in collaborative recommender systems: user bias, non-association, and cold start problems in capturing accurate similarities among items. Experimental results based on two real-world data sets lead us to believe that content information is crucial in achieving better personalized recommendation beyond user ratings. We further show how primitive audio features can be combined into aggregate features for the proposed CRMS and analyze their influences on recommendation performance. Although this model was developed originally for music collaborative recommendation based on audio features, our experiment with the movie data set demonstrates that it can be applied to other domains. 相似文献
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Paul Lehner Charles WorrellChrissy Vu Janet MittelStephen Snyder Eric SchulteWarren Greiff 《Information processing & management》2010
This paper describes an applied document filtering system embedded in an operational watch center that monitors disease outbreaks worldwide. At the initial time of this writing, the system effectively supported monitoring of 23 geographic regions by filtering documents in several thousand daily news sources in 11 different languages. This paper describes the filtering algorithm, statistical procedures for estimating Precision and Recall in an operational environment, summarizes operational performance data and suggests lessons learned for other applications of document filtering technology. Overall, these results are interpreted as supporting the general utility of document filtering and information retrieval technology and offers recommendations for future applications of this technology. 相似文献
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电子邮件已渐渐在网络通信中扮演极其重要的角色,成为构建Internet的基石之一。本文在对垃圾邮件做了简单介绍的基础上,分析了垃圾邮件在我国肆意泛滥的严重危害,并论述了现今被广泛使用的几种主流反垃圾邮件过滤技术。 相似文献
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基于协同过滤技术的个性化课程推荐系统研究 总被引：1，自引：0，他引：1
本文通过对个性化推荐技术涵义的叙述,提出基于个性化推荐技术中的协同过滤技术构筑个性化课程推荐系统平台,并且对系统平台的设计与实现过程进行了阐述。 相似文献
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网络不良信息过滤研究 总被引：9，自引：0，他引：9
探讨了目前国内外用于不良信息过滤的主要方法，包括分级法、URL地址列表法、文本内容过滤技术和多媒体信息过滤技术等，并对其优缺点进行了分析和比较。 相似文献
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Search engines, such as Google, assign scores to news articles based on their relevance to a query. However, not all relevant articles for the query may be interesting to a user. For example, if the article is old or yields little new information, the article would be uninteresting. Relevance scores do not take into account what makes an article interesting, which would vary from user to user. Although methods such as collaborative filtering have been shown to be effective in recommendation systems, in a limited user environment, there are not enough users that would make collaborative filtering effective. 相似文献
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Ismail Sengor Altingovde Özlem Nurcan Subakan Özgür Ulusoy 《Information processing & management》2013
In-memory nearest neighbor computation is a typical collaborative filtering approach for high recommendation accuracy. However, this approach is not scalable given the huge number of customers and items in typical commercial applications. Cluster-based collaborative filtering techniques can be a remedy for the efficiency problem, but they usually provide relatively lower accuracy figures, since they may become over-generalized and produce less-personalized recommendations. Our research explores an individualistic strategy which initially clusters the users and then exploits the members within clusters, but not just the cluster representatives, during the recommendation generation stage. We provide an efficient implementation of this strategy by adapting a specifically tailored cluster-skipping inverted index structure. Experimental results reveal that the individualistic strategy with the cluster-skipping index is a good compromise that yields high accuracy and reasonable scalability figures. 相似文献