共查询到19条相似文献，搜索用时 31 毫秒
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Zhang Fuguo 《情报学报》2012,31(9)
数据稀疏性多年来一直是困扰传统推荐系统性能表现的一个大问题,社会化标签为推荐系统获得用户的偏好信息提供了一个新的数据来源,同时也对传统的基于二维数据的推荐技术提出了新的挑战.不同于以往更多的以推荐标签为研究目标的是,本文以推荐项目(产品)为研究目的,在分析、评述社会化标签系统的概念模型以及用户兴趣模型表示方法基础之上,重点对基于标签的四种项目推荐方法进行了前沿概括、比较和分析;接着介绍了典型社会化标签系统实例及其数据集的取得方式;最后,对基于标签的个性化项目推荐系统有待深入的研究难点和发展趋势进行了展望. 相似文献
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由于人类信息处理的认知约束,用户很难在信息过载的电子商务网站中快速找到与其需求想匹配的产品.尽管推荐系统有着提高用户快速找到产品效率的潜力,但这种潜力能否最终实现,取决于用户能否有效地接受和使用推荐系统.本文首先对推荐内涵和类型进行了总结,重点对事前和事后两种推荐类型进行了分析;其次对目前推荐系统在使用过程中存在的问题进行了分析;再次对推荐技术用户接受行为研究进行了总结;然后基于用户认知理论对推荐技术用户接受行为机理进行了深入研究;最后构建基于B2C网站的影响因素模型及实证分析,分析了用户认知风格差异对模型的影响,并基于模型验证结果对卓越网推荐系统使用问题及影响因素进行分析. 相似文献
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个性化推荐在图书馆信息服务系统中的应用 总被引：1，自引：0，他引：1
个性化推荐被认为是解决信息过载最有效的工具,已经在高校图书馆信息服务系统中得到了广泛应用。对高校图书馆个性化推荐系统的应用现状、推荐方法进行了综述,并指出高校图书馆个性化推荐系统面临的问题和下一步研究的方向。 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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智能推荐已经成为人工智能（artificial intelligence,AI）学术研究和产业应用密切关注的研究问题。分析面向智能推荐的AI人机交互研究热点和未来机会,可更好地促进跨学科学者在该领域开展进一步的纵深和延展研究,了解用户视角下信息行为研究的最新进展。本文采用扎根文献综述方法,通过定义与检索研究问题、遴选文献集合、提取合成编码和分析结果展示4个分析环节,对64篇中英文文献进行分析综述,并分析了面向智能推荐场景的AI人机交互研究热点及未来机会。综述发现,研究热点聚焦在面向智能推荐的AI人机交互行为及影响、AI人机交互感知与情感表达以及AI人机交互场景与服务应用。未来研究可以围绕面向智能推荐的新型AI人机交互关系、AI人机交互形式、AI人机交互影响和AI人机交互设备展开,相关学者可以在上述4个方面开展纵深化和跨学科的协同研究。 相似文献
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协同信息推荐系统逐渐被应用到数字图书馆中并成为该领域的主要研究主题之一。从协同信息推荐技术本身、该技术在数字图书馆中的应用以及典型数字图书馆协同信息推荐系统研究等方面对相关研究进行分析和述评,并提出数字图书馆协同信息推荐技术应用的改进策略。 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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基于信任的电子商务推荐多样性研究 总被引：3，自引：0，他引：3
现有的推荐系统研究大都千方百计地关注于如何提高推荐算法的准确性,考虑到用户兴趣的覆盖范围,这样做的缺陷是只考虑了推荐列表中单个项目的准确度,而忽略了整个推荐列表多样性对用户满意度的影响.近几年的研究表明将信任机制融入到个性化推荐过程中对提高传统协同过滤技术的准确性和鲁棒性有积极的影响,本文提出了基于社会网络信任的推荐多样性算法,该算法通过选择主题多样性好的信任邻居来平衡推荐结果的准确性和多样性.一系列的实验结果表明,该算法能有效地提高推荐的多样性. 相似文献
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Collaborative filtering (CF) is a popular method for personalizing product recommendations for e-commerce applications. In order to recommend a product to a user and predict that user’s preference, CF utilizes product evaluation ratings of like-minded users. The process of finding like-minded users forms a social network among all users and each link between two users represents an implicit connection between them. Users having more connections with others are the most influential users. Attacking recommender systems is a new issue for these systems. Here, an attacker tries to manipulate a recommender system in order to change the recommendation output according to her wish. If an attacker succeeds, her profile is used over and over again by the recommender system, making her an influential user. In this study, we applied the established attack detection methods to the influential users, instead of the whole user set, to improve their attack detection performance. Experiments were conducted using the same settings previously used to test the established methods. The results showed that the proposed influence-based method had better detection performance and improved the stability of a recommender system for most attack scenarios. It performed considerably better than established detection methods for attacks that inserted low numbers of attack profiles (20–25 %). 相似文献
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There is an increasing consensus in the Recommender Systems community that the dominant error-based evaluation metrics are insufficient, and mostly inadequate, to properly assess the practical effectiveness of recommendations. Seeking to evaluate recommendation rankings—which largely determine the effective accuracy in matching user needs—rather than predicted rating values, Information Retrieval metrics have started to be applied for the evaluation of recommender systems. In this paper we analyse the main issues and potential divergences in the application of Information Retrieval methodologies to recommender system evaluation, and provide a systematic characterisation of experimental design alternatives for this adaptation. We lay out an experimental configuration framework upon which we identify and analyse specific statistical biases arising in the adaptation of Information Retrieval metrics to recommendation tasks, namely sparsity and popularity biases. These biases considerably distort the empirical measurements, hindering the interpretation and comparison of results across experiments. We develop a formal characterisation and analysis of the biases upon which we analyse their causes and main factors, as well as their impact on evaluation metrics under different experimental configurations, illustrating the theoretical findings with empirical evidence. We propose two experimental design approaches that effectively neutralise such biases to a large extent. We report experiments validating our proposed experimental variants, and comparing them to alternative approaches and metrics that have been defined in the literature with similar or related purposes. 相似文献