基于协同谱聚类的推荐系统托攻击防御算法

提出了一种基于协同谱聚类的推荐系统托攻击防御算法. 该算法首先使用谱聚类方法对协同聚类算法进行改进,以在用户和项目2个维度上同时进行聚类;接着在聚类基础上结合分级偏离平均度对用户进行项目推荐. 实验测试结果表明,在同等托攻击规模的情况下,该算法可以降低实施托攻击的用户和攻击数据对系统推荐结果的影响.     

Collaborative filtering based on information-theoretic co-clustering

Collaborative filtering is one of the most popular recommendation techniques, which provides personalised recommendations based on users’ tastes. In spite of its huge success, it suffers from a range of problems, the most fundamental being that of data sparsity. Sparsity in ratings makes the formation of inaccurate neighbourhood, thereby resulting in poor recommendations. To address this issue, in this article, we propose a novel collaborative filtering approach based on information-theoretic co-clustering. The proposed approach computes two types of similarities: cluster preference and rating, and combines them. Based on the combined similarity, the user-based and item-based approaches are adopted, respectively, to obtain individual predictions for an unknown target rating. Finally, the proposed approach fuses these resultant predictions. Experimental results show that the proposed approach is superior to existing alternatives.     

A novel framework to alleviate the sparsity problem in context-aware recommender systems

Recommender systems have become indispensable for services in the era of big data. To improve accuracy and satisfaction, context-aware recommender systems (CARSs) attempt to incorporate contextual information into recommendations. Typically, valid and influential contexts are determined in advance by domain experts or feature selection approaches. Most studies have focused on utilizing the unitary context due to the differences between various contexts. Meanwhile, multi-dimensional contexts will aggravate the sparsity problem, which means that the user preference matrix would become extremely sparse. Consequently, there are not enough or even no preferences in most multi-dimensional conditions. In this paper, we propose a novel framework to alleviate the sparsity issue for CARSs, especially when multi-dimensional contextual variables are adopted. Motivated by the intuition that the overall preferences tend to show similarities among specific groups of users and conditions, we first explore to construct one contextual profile for each contextual condition. In order to further identify those user and context subgroups automatically and simultaneously, we apply a co-clustering algorithm. Furthermore, we expand user preferences in a given contextual condition with the identified user and context clusters. Finally, we perform recommendations based on expanded preferences. Extensive experiments demonstrate the effectiveness of the proposed framework.     

一种融合社交网络的叠加联合聚类推荐模型

为解决用户冷启动问题并提高推荐算法的评分预测精度,提出一种融合社交网络的叠加联合聚类推荐模型(SN-ACCRec),将用户社交关系融合到对评分矩阵的用户聚类中。根据社交关系理论分析用户社交关系,采用模糊C均值聚类的思想划分用户块,并利用k均值算法对评分矩阵的产品聚类,得到一次联合聚类结果。通过迭代方式获取用户和产品多层联合聚类结果,不断叠加多层聚类结果来近似评分矩阵,预期先后得到用户和产品的泛化和细化类别,实现对评分矩阵中缺失值的预测。采用十重交叉验证法对模型评估,试验结果表明,该模型有效降低了推荐中的平均绝对误差(mean absolute error, MAE)和均方根误差(root mean square error, RMSE),同时在冷启动用户上也表现出了较好地推荐性能。     

基于联合聚类和矩阵分解的协同过滤算法研究

提出了基于联合聚类和带正则化的迭代最小二乘法的协同过滤算法。该算法对原始矩阵进行用户-项目两个维度的联合聚类生成若干子矩阵,子矩阵的规模远小于原始评分矩阵,可有效降低预测阶段计算量,而且也缓解了数据稀疏性问题。在子矩阵中通过对传统的矩阵分解进行正则化约束来防止模型过拟合现象,并采用迭代最小二乘法进行训练分解模型,可有效缓解可扩展性。实验表明,该方法具有高效性。     

基于co-ICIB联合聚类的舆情监测系统设计

基于co-ICIB联合聚类的舆情监测系统的设计为舆情信息库,它通过联合聚类等数据挖掘算法可以快速及时地发现新的舆论热点.当舆论热点被确认,即在互联网上真正成为一个备受关注的话题时,文本分类算法可以将同一话题内的信息归类,有助于跟踪舆情的发展趋势.该舆情监测系统可为舆情监管部门提供原始舆情资料、数据性图表和建议性分析.     

两阶段联合聚类协同过滤算法

提出一种两阶段评分预测方法.该方法基于一种新的联合聚类算法(BlockClust)和加权非负矩阵分解算法.首先对原始矩阵中的评分模式进行用户和物品两个维度的联合聚类,然后在这些类别的内部通过加权非负矩阵分解方法进行未知评分预测.这种方法的优势在于,首阶段聚类后的矩阵规模远远小于原始评分矩阵,并且同一类别内部的评分具有相似的模式,这样,在大幅度降低预测阶段计算量的同时又提高了非负矩阵分解算法在面对稀疏矩阵预测上的准确度.进一步给出了推荐系统的3种更新模式下如何高效更新预测模型的增量学习方法.在MovieLens数据集上比较了新算法及其他7种相关方法的性能,从而验证了该方法的有效性及其在大型实时推荐系统中的应用价值.     

高阶异构数据层次联合聚类算法

在实际应用中,包含多种特征空间信息的高阶异构数据广泛出现.由于高阶联合聚类算法能够有效融合多种特征空间信息提高聚类效果,近年来逐渐成为研究热点.目前高阶联合聚类算法多数为非层次聚类算法.然而,高阶异构数据内部往往隐藏着层次聚簇结构,为了更有效地挖掘数据内部隐藏的层次聚簇模式,提出了一种高阶层次联合聚类算法(high-order hierarchical co-clustering algorithm,HHCC).该算法利用变量相关性度量指标Goodman-Kruskal τ衡量对象变量和特征变量的相关性,将相关性较强的对象划分到同一个对象聚簇中,同时将相关性较强的特征划分到同一个特征聚簇中.HHCC算法采用自顶向下的分层聚类策略,利用指标Goodman-Kruskal τ评估每层对象和特征的聚类质量,利用局部搜索方法优化指标Goodman-Kruskal τ,自动确定聚簇数目,获得每层的聚类结果,最终形成树状聚簇结构.实验结果表明HHCC算法的聚类效果优于4种经典的同构层次聚类算法和5种已有的非层次高阶联合聚类算法.     

基于电阻网络的异构数据协同聚类算法

传统聚类方法处理的是同构数据,无法满足异构数据同时聚类的应用需求,聚类结果的准确率较低,标签可读性较差。针对上述问题,提出一种基于电阻网络的异构数据协同聚类算法。该算法将异构关联数据抽象为多部图形式的电阻网络,进行特征计算及聚类。在对异构数据进行协同聚类后,可以得到一种聚类结构,其中每一类包含多种异构数据,它们之间可以互为标签,标签可读性高。实验结果证明,该方法是一种切实可行且效果优异的数据聚类算法。     

BEIRA: An Area-based User Interface for Map Services

This paper introduces BEIRA, an area-based map user interface for location-based contents. Recently, various web map services are widely used to search for location-based contents. However, browsing a large number of contents that are arranged on a map as points may be troublesome. We tackle this issue by using area-based representations instead of points. AOI (Area of Interest), which is core concept of BEIRA, is an arbitrary shaped area boundary with text summary information. With AOI, users can instantly grasp area characteristics without examining each point. AOI is deduced by performing geo-semantic co-clustering of location-based contents. Geo-semantic co-clustering takes both geographic and semantic features of contents into account. We confirm that the ratio of the geo-semantic blend is the key to deducing an appropriate boundary. We further propose and evaluate location-aware term weighting to obtain an informative summary.