共查询到20条相似文献,搜索用时 15 毫秒
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吕成戍 《计算机工程与科学》2014,36(4):697-701
基于标准支持向量机的托攻击检测方法不能体现由于用户误分代价不同对分类效果带来的影响,提出了一种基于代价敏感支持向量机的托攻击检测新方法,该方法在代价敏感性学习机制下引入支持向量机作为分类工具,对支持向量机输出进行后验概率建模,建立了基于类别隶属度的动态代价函数,更准确地反映不同样本的分类代价,在此基础上设计了代价敏感支持向量机分类器。将该分类器应用在推荐系统托攻击检测中,并与标准的支持向量机方法、代价敏感支持向量机方法进行比较,实验结果表明,本方法可以更精确地控制代价敏感性,进一步提高对攻击用户的检测精度,降低总体的误分类代价。 相似文献
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To alleviate the problem of data sparsity inherent to recommender systems, we propose a semi-supervised framework for stream-based recommendations. Our framework uses abundant unlabelled information to improve the quality of recommendations. We extend a state-of-the-art matrix factorization algorithm by the ability to add new dimensions to the matrix at runtime and implement two approaches to semi-supervised learning: co-training and self-learning. We introduce a new evaluation protocol including statistical testing and parameter optimization. We then evaluate our framework on five real-world datasets in a stream setting. On all of the datasets our method achieves statistically significant improvements in the quality of recommendations. 相似文献
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In the Big Data Era, recommender systems perform a fundamental role in data management and information filtering. In this context, Collaborative Filtering (CF) persists as one of the most prominent strategies to effectively deal with large datasets and is capable of offering users interesting content in a recommendation fashion. Nevertheless, it is well-known CF recommenders suffer from data sparsity, mainly in cold-start scenarios, substantially reducing the quality of recommendations. In the vast literature about the aforementioned topic, there are numerous solutions, in which the state-of-the-art contributions are, in some sense, conditioned or associated with traditional CF methods such as Matrix Factorization (MF), that is, they rely on linear optimization procedures to model users and items into low-dimensional embeddings. To overcome the aforementioned challenges, there has been an increasing number of studies exploring deep learning techniques in the CF context for latent factor modelling. In this research, authors conduct a systematic review focusing on state-of-the-art literature on deep learning techniques applied in collaborative filtering recommendation, and also featuring primary studies related to mitigating the cold start problem. Additionally, authors considered the diverse non-linear modelling strategies to deal with rating data and side information, the combination of deep learning techniques with traditional CF-based linear methods, and an overview of the most used public datasets and evaluation metrics concerning CF scenarios. 相似文献
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针对老人跌倒时的复杂运动情况,进行跌倒标注的较难实现,提出了基于Tri-training半监督算法的跌倒检测系统。本系统使用3D加速度传感器采集运动加速度数据,然后对数据进行特征提取与部分样本标注,使用Tri-training算法训练分类器,最后使用训练好的分类器进行跌倒识别。具体的数据采集传感器设计为可穿戴式设备,服务器端使用Java编写了一个服务器的程序实现对数据的分析与处理。实验结果表明:该方法使用了大量无标签数据的信息,有效提高了跌倒识别的准确率。实验结果表明:本系统能够满足老年人在日常生活中的需求,对于一些意外跌倒能够给予及时的检测与报警。 相似文献
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Alqadah Faris Reddy Chandan K. Hu Junling Alqadah Hatim F. 《Knowledge and Information Systems》2015,44(2):475-491
Knowledge and Information Systems - We propose a novel collaborative filtering method for top- $$n$$ recommendation task using bicustering neighborhood approach. Our method takes advantage of local... 相似文献
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Almost unlimited access to educational information plethora came with a drawback: finding meaningful material is not a straightforward task anymore. Based on a survey related to how students find additional bibliographical resources for university courses, we concluded there is a strong need for recommended learning materials, for specialized online search and for personalized learning tools. As a result, we developed an educational collaborative filtering recommender agent, with an integrated learning style finder. The agent produces two types of recommendations: suggestions and shortcuts for learning materials and learning tools, helping the learner to better navigate through educational resources. Shortcuts are created taking into account only the user’s profile, while suggestions are created using the choices made by the learners with similar learning styles. The learning style finder assigns to each user a profile model, taking into account an index of learning styles, as well as patterns discovered in the virtual behavior of the user. The current study presents the agent itself, as well as its integration to a virtual collaborative learning environment and its success and limitations, based on users’ feedback. 相似文献
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针对目前大多推荐系统中使用的协同过滤算法都需要有显示的用户反馈的问题,提出一种在隐式反馈推荐系统中使用聚类与矩阵分解技术相结合的方法,为用户提供更好地推荐结果。其结果是由基于用户历史购买记录的隐式反馈产生的,不需任何显式反馈提供的数据。采用高维的、无参数的分裂层次聚类技术产生聚类结果,根据聚类的结果为每个用户提供高兴趣度的个性化推荐。实验结果表明,在隐式反馈的情况下该方法也能有效获得用户偏好,产生大量的高准确度推荐。 相似文献
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Collaborative learning incorporates a social component in distance education to minimize the disadvantages of studying in solitude. Frequent analysis of student interactions is required for assessing collaboration. Collaboration analytics arose as a discipline to study student interactions and to promote active participation in e-learning environments. Unfortunately, researchers have been more focused on finding methods to solve collaboration problems than on explaining the results to tutors and students. Yet if students do not understand the results of collaboration analysis methods, they will rarely follow their advice. In this paper we propose a tool that analyzes student interactions and visually explains the collaboration circumstances to provoke the self-reflection and promote the sensemaking about collaboration. The tool presents a visual explanatory decision tree that graphically highlights student collaboration circumstances and helps to understand the reasoning followed by the tool when prescribing a recommendation. An assessment of the tool has demonstrated: (1) the students collaboration circumstances showed by the tool are easy to understand and (2) the students could realize the possible actions to improve the collaboration process. 相似文献
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Collaborative filtering plays the key role in recent recommender systems. It uses a user-item preference matrix rated either explicitly (i.e., explicit rating) or implicitly (i.e., implicit feedback). Despite the explicit rating captures the preferences better, it often results in a severely sparse matrix. The paper presents a novel iterative semi-explicit rating method that extrapolates unrated elements in a semi-supervised manner. Extrapolation is simply an aggregation of neighbor ratings, and iterative extrapolations result in a dense preference matrix. Preliminary simulation results show that the recommendation using the semi-explicit rating data outperforms that of using the pure explicit data only. 相似文献
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Recommender systems are emerging techniques guiding individuals with provided referrals by considering their past rating behaviors. By collecting multi-criteria preferences concentrating on distinguishing perspectives of the items, a new extension of traditional recommenders, multi-criteria recommender systems reveal how much a user likes an item and why user likes it; thus, they can improve predictive accuracy. However, these systems might be more vulnerable to malicious attacks than traditional ones, as they expose multiple dimensions of user opinions on items. Attackers might try to inject fake profiles into these systems to skew the recommendation results in favor of some particular items or to bring the system into discredit. Although several methods exist to defend systems against such attacks for traditional recommenders, achieving robust systems by capturing shill profiles remains elusive for multi-criteria rating-based ones. Therefore, in this study, we first consider a prominent and novel attack type, that is, the power-item attack model, and introduce its four distinct variants adapted for multi-criteria data collections. Then, we propose a classification method detecting shill profiles based on various generic and model-based user attributes, most of which are new features usually related to item popularity and distribution of rating values. The experiments conducted on three benchmark datasets conclude that the proposed method successfully detects attack profiles from genuine users even with a small selected size and attack size. The empirical outcomes also demonstrate that item popularity and user characteristics based on their rating profiles are highly beneficial features in capturing shilling attack profiles. 相似文献
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Graph-based semi-supervised learning is an important semi-supervised learning paradigm. Although graph-based semi-supervised learning methods have been shown to be helpful in various situations, they may adversely affect performance when using unlabeled data. In this paper, we propose a new graph-based semi-supervised learning method based on instance selection in order to reduce the chances of performance degeneration. Our basic idea is that given a set of unlabeled instances, it is not the best approach to exploit all the unlabeled instances; instead, we should exploit the unlabeled instances that are highly likely to help improve the performance, while not taking into account the ones with high risk. We develop both transductive and inductive variants of our method. Experiments on a broad range of data sets show that the chances of performance degeneration of our proposed method are much smaller than those of many state-of-the-art graph-based semi-supervised learning methods. 相似文献
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Shukla Prashant Abhishek Verma Shekhar Kumar Manish 《Pattern Analysis & Applications》2021,24(3):887-905
Pattern Analysis and Applications - In manifold learning, the intrinsic geometry of the manifold is explored and preserved by identifying the optimal local neighborhood around each observation. It... 相似文献
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With the rapid increasing of learning materials and learning objects in e-learning, the need for recommender system has also
become more and more imperative. Although, the traditional recommendation system has achieved great success in many domains,
it is not suitable to support e-learning recommender system because the approach in e-learning is hybrid and it is obtained
mainly by two mechanisms: the learners’ learning processes and the analysis of social interaction. Therefore, this study proposes
a flexible recommendation approach to satisfy this demand. The recommendation is designed based on a multidimensional recommendation
model. Furthermore, we use Markov Chain Model to divide the group learners into advanced learners and beginner learners by
using the learners’ learning activities and learning processes so that we can correctly estimate the rating which also include
learners’ social interaction. The experimental result shows that the proposed system can give a more satisfying and qualified
recommendation. 相似文献
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Neural Computing and Applications - The main purpose of collaborative filtering algorithm is to provide a personalized recommender system based on past interactions of each user (e.g., clicks and... 相似文献