共查询到18条相似文献，搜索用时 156 毫秒
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基于Ontology的文档过滤研究 总被引：2，自引：0，他引：2
区分文档过滤、信息过滤和文本过滤并介绍文档过滤技术的研究现状;提出基于Ontology的文档过滤的设想,认为其优势在于灵活、共享性好、有利于进行个性化服务等;讨论基于Ontology的文档过滤的实施过程,包括构建准备、本体构建、本体调用,重点阐述公共本体、用户本体和文档本体的构建方法以及实施过程中涉及的技术体系;最后指出今后的努力方向。 相似文献
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区分文档过滤、信息过滤和文本过滤并介绍文档过滤技术的研究现状；提出基于Ontology的文档过滤的设想，认为其优势在于灵活、共享性好、有利于进行个性化服务等；讨论基于Ontology的文档过滤的实施过程，包括构建准备、本体构建、本体调用，重点阐述公共本体、用户本体和文档本体的构建方法以及实施过程中涉及的技术体系；最后指出今后的努力方向。 相似文献
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针对传统TF-IDF在文本过滤时存在的缺点,提出一种基于特征词抽取的文本过滤算法。简要分析文档信息过滤原理和流程,重点讨论文档信息过滤算法设计及技术实现。实验结果表明,所提出的算法可有效对文档信息进行过滤,能够提高信息检索质量。 相似文献
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本文对文本型的电子文件保护技术进行了探讨,提出了简单而有效的文档保护算法,其保护重点在于其一致性和完整性,算法的基础是基于哈夫曼树的二进制编码,通过对原始电子文件的二进制压缩编码形成了以压缩文件为中心的,包括原始文档、压缩文件,数字指纹三为一体的文档保护机制,从而保证了文档的一致性与完整性。 相似文献
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基于序列模式的个性化Web页面推荐模型* 总被引：1，自引：1，他引：0
易明 《现代图书情报技术》2008,24(8):42-47
基于数据挖掘中的序列模式方法,提出一种个性化Web页面推荐模型。该模型首先利用Web使用数据预处理提取Web交易事务集,然后应用序列模式算法挖掘频繁（连续）序列,最后通过构建频繁（连续）序列树生成用户偏好视图以生成个性化Web页面推荐集。 相似文献
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为促进学生思考并提高响应速度,提出一种从历史研讨记录中挖掘相关信息的在线问答推荐方法。该方法包括建立技术词汇层次树、提取任务词汇、文本段落划分、特征抽取、主题识别过滤和计算文档得分6个步骤。通过设计两个实验来评估所提出的方法:第一个实验比较TF-IDF、TF-IDF+主题过滤以及TF-IDF+LSA+主题过滤三种推荐方法,结果表明使用TF-IDF+主题过滤的算法可以获得最好的推荐效果;第二个实验将系统用于一个学期的在线课程研讨中,现场评估结果表明,文档推荐系统可以促进学生研讨,并且有较高的感知有用性和易用性。本研究表明,中等相关程度的历史研讨记录可以被自动挖掘出来,并且向学生提供这些信息可以促进学生思考和研讨。 相似文献
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统计频率算法在文本信息过滤系统中的应用 总被引：1，自引：0，他引：1
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拟合用户兴趣演变特性的协作过滤推荐算法 总被引：2，自引：0，他引：2
个性化推荐技术是将传统的数据挖掘技术同用户访问信息结合起来,根据用户的兴趣爱好来对用户可能访问的内容进行预测并预取其提供给用户进行选择.目前协作过滤技术是个性化推荐系统中应用最为成功的推荐技术之一,但传统的协作过滤算法没有考虑用户的兴趣演变,难以有效地反映用户真实兴趣.在分析目前协作过滤算法存在问题的基础上,利用用户访问兴趣分为偶然兴趣和稳定兴趣的特性,文章提出了基于偶然兴趣的推荐权重和基于稳定兴趣的推荐权重,并将它们融入新的拟合用户兴趣演变的协作过滤算法中.实验表明该算法能准确地反映用户访问兴趣,较传统的协作过滤算法可以有效提高推荐精度. 相似文献
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When speaking of information retrieval, we often mean text retrieval. But there exist many other forms of information retrieval applications. A typical example is collaborative filtering that suggests interesting items to a user by taking into account other users’ preferences or tastes. Due to the uniqueness of the problem, it has been modeled and studied differently in the past, mainly drawing from the preference prediction and machine learning view point. A few attempts have yet been made to bring back collaborative filtering to information (text) retrieval modeling and subsequently new interesting collaborative filtering techniques have been thus derived. In this paper, we show that from the algorithmic view point, there is an even closer relationship between collaborative filtering and text retrieval. Specifically, major collaborative filtering algorithms, such as the memory-based, essentially calculate the dot product between the user vector (as the query vector in text retrieval) and the item rating vector (as the document vector in text retrieval). Thus, if we properly structure user preference data and employ the target user’s ratings as query input, major text retrieval algorithms and systems can be directly used without any modification. In this regard, we propose a unified formulation under a common notational framework for memory-based collaborative filtering, and a technique to use any text retrieval weighting function with collaborative filtering preference data. Besides confirming the rationale of the framework, our preliminary experimental results have also demonstrated the effectiveness of the approach in using text retrieval models and systems to perform item ranking tasks in collaborative filtering. 相似文献
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基于社会化标签系统的个性化信息推荐探讨 总被引：4，自引：0，他引：4
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一个新的基于协作过滤的用户浏览预测模型 总被引：2，自引：0，他引：2
本文提出了一个新的基于协作过滤的用户浏览协作预测模型———UNCPM ,它有效地解决了目前协作过滤预测方法的准确性和覆盖率低等问题。UNCPM从Web日志中获取用户浏览信息 ,系统分为两个部分 :离线构件和在线构件。离线构件用于用户浏览历史记录的K means聚类 ,并在聚类时充分考虑URL的相似分析来避免协作过滤的同义性和分散性等不足 ;在线构件用于活动用户预测。该模型可以应用在大型电子商务网站的用户浏览预测上。 相似文献
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Searching online information resources using mobile devices is affected by small screens which can display only a fraction
of ranked search results. In this paper we investigate whether the search effort can be reduced by means of a simple user
feedback: for a screenful of search results the user is encouraged to indicate a single most relevant document. In our approach
we exploit the fact that, for small display sizes and limited user actions, we can construct a user decision tree representing
all possible outcomes of the user interaction with the system. Examining the trees we can compute an upper limit on relevance
feedback performance. In this study we consider three standard feedback algorithms: Rocchio, Robertson/Sparck-Jones (RSJ)
and a Bayesian algorithm. We evaluate them in conjunction with two strategies for presenting search results: a document ranking
that attempts to maximize information gain from the user’s choices and the top-D ranked documents. Experimental results indicate
that for RSJ feedback which involves an explicit feature selection policy, the greedy top-D display is more appropriate. For
the other two algorithms, the exploratory display that maximizes information gain produces better results. We conducted a
user study to compare the performance of the relevance feedback methods with real users and compare the results with the findings
from the tree analysis. This comparison between the simulations and real user behaviour indicates that the Bayesian algorithm,
coupled with the sampled display, is the most effective.
Extended version of “Evaluating Relevance Feedback Algorithms for Searching on Small Displays, ” Vishwa Vinay, Ingemar J.
Cox, Natasa Milic-Frayling, Ken Wood published in the proceedings of ECIR 2005, David E. Losada, Juan M. Fernández-Luna (Eds.),
Springer 2005, ISBN 3-540-25295-9 相似文献
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数字图书馆信息过滤系统初探* 总被引：5，自引：0，他引：5
信息过滤技术体现个性化服务的思想, 可以作为数字图书馆推荐系统信息服务的一种解决方法。本文对数字图书馆系统中用户描述文件和文献描述文件的建立与完善及其相互匹配机制进行分析和研究, 并对数字图书馆的信息过滤系统进行了初步的探讨。 相似文献
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Stephen Robertson 《Information Retrieval》2002,5(2-3):127-137
This paper introduces the special issue, and reviews the routing and filtering tasks as defined and evaluated at TREC. The tasks attempt to simulate a specific service situation: the system is assumed to process an incoming stream of documents against profiles of user interest, strictly in the time order in which they arrive, and immediately refer any matching document to the user. In the adaptive filtering version of the task, the user is assumed to provide a relevance judgement instantly. The rationale for the task definitions and the evaluation measures used is discussed. 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